| 2019 | (Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs. Boaz Barak, Chi-Ning Chou, Zhixian Lei, Tselil Schramm, Yueqi Sheng |
| 2019 | A Bayesian Theory of Conformity in Collective Decision Making. Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao |
| 2019 | A Benchmark for Interpretability Methods in Deep Neural Networks. Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim |
| 2019 | A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers. Hao Yu |
| 2019 | A Composable Specification Language for Reinforcement Learning Tasks. Kishor Jothimurugan, Rajeev Alur, Osbert Bastani |
| 2019 | A Condition Number for Joint Optimization of Cycle-Consistent Networks. Leonidas J. Guibas, Qixing Huang, Zhenxiao Liang |
| 2019 | A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks. Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang |
| 2019 | A Debiased MDI Feature Importance Measure for Random Forests. Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, Bin Yu |
| 2019 | A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. Arun Jambulapati, Aaron Sidford, Kevin Tian |
| 2019 | A Domain Agnostic Measure for Monitoring and Evaluating GANs. Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian J. Goodfellow, Thomas Hofmann, Andreas Krause |
| 2019 | A Family of Robust Stochastic Operators for Reinforcement Learning. Yingdong Lu, Mark S. Squillante, Chai Wah Wu |
| 2019 | A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression. Jiajin Li, Sen Huang, Anthony Man-Cho So |
| 2019 | A Flexible Generative Framework for Graph-based Semi-supervised Learning. Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei |
| 2019 | A Fourier Perspective on Model Robustness in Computer Vision. Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin Dogus Cubuk, Justin Gilmer |
| 2019 | A Game Theoretic Approach to Class-wise Selective Rationalization. Shiyu Chang, Yang Zhang, Mo Yu, Tommi S. Jaakkola |
| 2019 | A General Framework for Symmetric Property Estimation. Moses Charikar, Kirankumar Shiragur, Aaron Sidford |
| 2019 | A General Theory of Equivariant CNNs on Homogeneous Spaces. Taco S. Cohen, Mario Geiger, Maurice Weiler |
| 2019 | A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation. Runzhe Yang, Xingyuan Sun, Karthik Narasimhan |
| 2019 | A Generic Acceleration Framework for Stochastic Composite Optimization. Andrei Kulunchakov, Julien Mairal |
| 2019 | A Geometric Perspective on Optimal Representations for Reinforcement Learning. Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle |
| 2019 | A Graph Theoretic Additive Approximation of Optimal Transport. Nathaniel Lahn, Deepika Mulchandani, Sharath Raghvendra |
| 2019 | A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation. Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama |
| 2019 | A Kernel Loss for Solving the Bellman Equation. Yihao Feng, Lihong Li, Qiang Liu |
| 2019 | A Latent Variational Framework for Stochastic Optimization. Philippe Casgrain |
| 2019 | A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning. Zhihui Zhu, Tianyu Ding, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal |
| 2019 | A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization. Sulaiman A. Alghunaim, Kun Yuan, Ali H. Sayed |
| 2019 | A Little Is Enough: Circumventing Defenses For Distributed Learning. Gilad Baruch, Moran Baruch, Yoav Goldberg |
| 2019 | A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off. Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry |
| 2019 | A Meta-Analysis of Overfitting in Machine Learning. Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt |
| 2019 | A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. Francisco M. Garcia, Philip S. Thomas |
| 2019 | A Model to Search for Synthesizable Molecules. John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato |
| 2019 | A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation. Xueying Bai, Jian Guan, Hongning Wang |
| 2019 | A Necessary and Sufficient Stability Notion for Adaptive Generalization. Moshe Shenfeld, Katrina Ligett |
| 2019 | A New Defense Against Adversarial Images: Turning a Weakness into a Strength. Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger |
| 2019 | A New Distribution on the Simplex with Auto-Encoding Applications. Andrew Stirn, Tony Jebara, David A. Knowles |
| 2019 | A New Perspective on Pool-Based Active Classification and False-Discovery Control. Lalit Jain, Kevin Jamieson |
| 2019 | A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution. Qing Qu, Xiao Li, Zhihui Zhu |
| 2019 | A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits. Wenhao Zhang, Si Wu, Brent Doiron, Tai Sing Lee |
| 2019 | A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant |
| 2019 | A Primal Dual Formulation For Deep Learning With Constraints. Yatin Nandwani, Abhishek Pathak, Mausam, Parag Singla |
| 2019 | A Primal-Dual link between GANs and Autoencoders. Hisham Husain, Richard Nock, Robert C. Williamson |
| 2019 | A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. Maksim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alexander Zhebrak |
| 2019 | A Refined Margin Distribution Analysis for Forest Representation Learning. Shen-Huan Lyu, Liang Yang, Zhi-Hua Zhou |
| 2019 | A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. Wenhao Yang, Xiang Li, Zhihua Zhang |
| 2019 | A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions. Yuan Deng, Sébastien Lahaie, Vahab S. Mirrokni |
| 2019 | A Self Validation Network for Object-Level Human Attention Estimation. Zehua Zhang, Chen Yu, David J. Crandall |
| 2019 | A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit. Yanis Bahroun, Dmitri B. Chklovskii, Anirvan M. Sengupta |
| 2019 | A Simple Baseline for Bayesian Uncertainty in Deep Learning. Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson |
| 2019 | A Solvable High-Dimensional Model of GAN. Chuang Wang, Hong Hu, Yue M. Lu |
| 2019 | A Step Toward Quantifying Independently Reproducible Machine Learning Research. Edward Raff |
| 2019 | A Stochastic Composite Gradient Method with Incremental Variance Reduction. Junyu Zhang, Lin Xiao |
| 2019 | A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning. Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric |
| 2019 | A Tensorized Transformer for Language Modeling. Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, Dawei Song |
| 2019 | A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya |
| 2019 | A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. Xuanqing Liu, Si Si, Jerry Zhu, Yang Li, Cho-Jui Hsieh |
| 2019 | A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening. Gecia Bravo Hermsdorff, Lee M. Gunderson |
| 2019 | A Universally Optimal Multistage Accelerated Stochastic Gradient Method. Necdet Serhat Aybat, Alireza Fallah, Mert Gürbüzbalaban, Asuman E. Ozdaglar |
| 2019 | A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid |
| 2019 | A coupled autoencoder approach for multi-modal analysis of cell types. Rohan Gala, Nathan W. Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar Sümbül |
| 2019 | A neurally plausible model for online recognition and postdiction in a dynamical environment. Li Kevin Wenliang, Maneesh Sahani |
| 2019 | A neurally plausible model learns successor representations in partially observable environments. Eszter Vértes, Maneesh Sahani |
| 2019 | A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI. Tao Tu, John W. Paisley, Stefan Haufe, Paul Sajda |
| 2019 | A unified theory for the origin of grid cells through the lens of pattern formation. Ben Sorscher, Gabriel Mel, Surya Ganguli, Samuel A. Ocko |
| 2019 | A unified variance-reduced accelerated gradient method for convex optimization. Guanghui Lan, Zhize Li, Yi Zhou |
| 2019 | ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. Jinjin Tian, Aaditya Ramdas |
| 2019 | AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling. Bichuan Guo, Yuxing Han, Jiangtao Wen |
| 2019 | ANODEV2: A Coupled Neural ODE Framework. Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros |
| 2019 | Abstract Reasoning with Distracting Features. Kecheng Zheng, Zheng-Jun Zha, Wei Wei |
| 2019 | Abstraction based Output Range Analysis for Neural Networks. Pavithra Prabhakar, Zahra Rahimi Afzal |
| 2019 | Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions. Ashia C. Wilson, Lester Mackey, Andre Wibisono |
| 2019 | Acceleration via Symplectic Discretization of High-Resolution Differential Equations. Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan |
| 2019 | Accurate Layerwise Interpretable Competence Estimation. Vickram Rajendran, William Levine |
| 2019 | Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. Jeremiah Z. Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull |
| 2019 | Accurate, reliable and fast robustness evaluation. Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge |
| 2019 | Adapting Neural Networks for the Estimation of Treatment Effects. Claudia Shi, David M. Blei, Victor Veitch |
| 2019 | Adaptive Auxiliary Task Weighting for Reinforcement Learning. Xingyu Lin, Harjatin Singh Baweja, George Kantor, David Held |
| 2019 | Adaptive Cross-Modal Few-shot Learning. Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro |
| 2019 | Adaptive Density Estimation for Generative Models. Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek |
| 2019 | Adaptive GNN for Image Analysis and Editing. Lingyu Liang, Lianwen Jin, Yong Xu |
| 2019 | Adaptive Gradient-Based Meta-Learning Methods. Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar |
| 2019 | Adaptive Influence Maximization with Myopic Feedback. Binghui Peng, Wei Chen |
| 2019 | Adaptive Sequence Submodularity. Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi |
| 2019 | Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu |
| 2019 | Adaptively Aligned Image Captioning via Adaptive Attention Time. Lun Huang, Wenmin Wang, Yaxian Xia, Jie Chen |
| 2019 | Addressing Failure Prediction by Learning Model Confidence. Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, Patrick Pérez |
| 2019 | Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs. Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin |
| 2019 | Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett |
| 2019 | Adversarial Examples Are Not Bugs, They Are Features. Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry |
| 2019 | Adversarial Fisher Vectors for Unsupervised Representation Learning. Shuangfei Zhai, Walter Talbott, Carlos Guestrin, Joshua M. Susskind |
| 2019 | Adversarial Music: Real world Audio Adversary against Wake-word Detection System. Juncheng Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter, Florian Metze |
| 2019 | Adversarial Robustness through Local Linearization. Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli |
| 2019 | Adversarial Self-Defense for Cycle-Consistent GANs. Dina Bashkirova, Ben Usman, Kate Saenko |
| 2019 | Adversarial Training and Robustness for Multiple Perturbations. Florian Tramèr, Dan Boneh |
| 2019 | Adversarial training for free! Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein |
| 2019 | Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. Spencer Frei, Yuan Cao, Quanquan Gu |
| 2019 | Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing. Zhiqi Bu, Jason M. Klusowski, Cynthia Rush, Weijie J. Su |
| 2019 | Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors. Gauri Jagatap, Chinmay Hegde |
| 2019 | Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations. Fenglin Liu, Yuanxin Liu, Xuancheng Ren, Xiaodong He, Xu Sun |
| 2019 | Alleviating Label Switching with Optimal Transport. Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon, Mikhail Yurochkin |
| 2019 | Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model. Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill |
| 2019 | Amortized Bethe Free Energy Minimization for Learning MRFs. Sam Wiseman, Yoon Kim |
| 2019 | An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums. Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié |
| 2019 | An Adaptive Empirical Bayesian Method for Sparse Deep Learning. Wei Deng, Xiao Zhang, Faming Liang, Guang Lin |
| 2019 | An Algorithm to Learn Polytree Networks with Hidden Nodes. Firoozeh Sepehr, Donatello Materassi |
| 2019 | An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors. Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin |
| 2019 | An Embedding Framework for Consistent Polyhedral Surrogates. Jessica Finocchiaro, Rafael M. Frongillo, Bo Waggoner |
| 2019 | An Improved Analysis of Training Over-parameterized Deep Neural Networks. Difan Zou, Quanquan Gu |
| 2019 | An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints. Mehmet Fatih Sahin, Armin Eftekhari, Ahmet Alacaoglu, Fabian Latorre, Volkan Cevher |
| 2019 | An adaptive Mirror-Prox method for variational inequalities with singular operators. Kimon Antonakopoulos, Elena Veronica Belmega, Panayotis Mertikopoulos |
| 2019 | An adaptive nearest neighbor rule for classification. Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund, Shay Moran |
| 2019 | Anti-efficient encoding in emergent communication. Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni |
| 2019 | Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse. Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens |
| 2019 | Approximate Feature Collisions in Neural Nets. Ke Li, Tianhao Zhang, Jitendra Malik |
| 2019 | Approximate Inference Turns Deep Networks into Gaussian Processes. Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa |
| 2019 | Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems. Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones, Àgata Lapedriza, Rosalind W. Picard |
| 2019 | Approximating the Permanent by Sampling from Adaptive Partitions. Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon |
| 2019 | Approximation Ratios of Graph Neural Networks for Combinatorial Problems. Ryoma Sato, Makoto Yamada, Hisashi Kashima |
| 2019 | Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration. Jianchun Chen, Lingjing Wang, Xiang Li, Yi Fang |
| 2019 | Are Anchor Points Really Indispensable in Label-Noise Learning? Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama |
| 2019 | Are Disentangled Representations Helpful for Abstract Visual Reasoning? Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem |
| 2019 | Are Labels Required for Improving Adversarial Robustness? Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, Pushmeet Kohli |
| 2019 | Are Sixteen Heads Really Better than One? Paul Michel, Omer Levy, Graham Neubig |
| 2019 | Are deep ResNets provably better than linear predictors? Chulhee Yun, Suvrit Sra, Ali Jadbabaie |
| 2019 | Are sample means in multi-armed bandits positively or negatively biased? Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo |
| 2019 | Ask not what AI can do, but what AI should do: Towards a framework of task delegability. Brian Lubars, Chenhao Tan |
| 2019 | Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. Nathan Kallus, Angela Zhou |
| 2019 | Assessing Social and Intersectional Biases in Contextualized Word Representations. Yi Chern Tan, L. Elisa Celis |
| 2019 | Asymmetric Valleys: Beyond Sharp and Flat Local Minima. Haowei He, Gao Huang, Yang Yuan |
| 2019 | Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau |
| 2019 | Asymptotics for Sketching in Least Squares Regression. Edgar Dobriban, Sifan Liu |
| 2019 | AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu |
| 2019 | Attentive State-Space Modeling of Disease Progression. Ahmed M. Alaa, Mihaela van der Schaar |
| 2019 | Attribution-Based Confidence Metric For Deep Neural Networks. Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Brian Jalaian, Gunjan Verma, Ananthram Swami |
| 2019 | Augmented Neural ODEs. Emilien Dupont, Arnaud Doucet, Yee Whye Teh |
| 2019 | AutoAssist: A Framework to Accelerate Training of Deep Neural Networks. Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon |
| 2019 | AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters. Xia Xiao, Zigeng Wang, Sanguthevar Rajasekaran |
| 2019 | Average Case Column Subset Selection for Entrywise 퓁 Zhao Song, David P. Woodruff, Peilin Zhong |
| 2019 | Average Individual Fairness: Algorithms, Generalization and Experiments. Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth |
| 2019 | Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. Mark Bun, Thomas Steinke |
| 2019 | BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling. Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther |
| 2019 | Backprop with Approximate Activations for Memory-efficient Network Training. Ayan Chakrabarti, Benjamin Moseley |
| 2019 | Backpropagation-Friendly Eigendecomposition. Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann |
| 2019 | Balancing Efficiency and Fairness in On-Demand Ridesourcing. Nixie S. Lesmana, Xuan Zhang, Xiaohui Bei |
| 2019 | Band-Limited Gaussian Processes: The Sinc Kernel. Felipe A. Tobar |
| 2019 | Bandits with Feedback Graphs and Switching Costs. Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri |
| 2019 | Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes. Gunpil Hwang, Seohyeon Kim, Hyeon-Min Bae |
| 2019 | BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. Andreas Kirsch, Joost van Amersfoort, Yarin Gal |
| 2019 | Batched Multi-armed Bandits Problem. Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou |
| 2019 | Bayesian Batch Active Learning as Sparse Subset Approximation. Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato |
| 2019 | Bayesian Joint Estimation of Multiple Graphical Models. Lingrui Gan, Xinming Yang, Naveen N. Narisetty, Feng Liang |
| 2019 | Bayesian Layers: A Module for Neural Network Uncertainty. Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner |
| 2019 | Bayesian Learning of Sum-Product Networks. Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani |
| 2019 | Bayesian Optimization under Heavy-tailed Payoffs. Sayak Ray Chowdhury, Aditya Gopalan |
| 2019 | Bayesian Optimization with Unknown Search Space. Huong Ha, Santu Rana, Sunil Gupta, Thanh Tang Nguyen, Hung Tran-The, Svetha Venkatesh |
| 2019 | Beating SGD Saturation with Tail-Averaging and Minibatching. Nicole Mücke, Gergely Neu, Lorenzo Rosasco |
| 2019 | BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey E. Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski |
| 2019 | Better Exploration with Optimistic Actor Critic. Kamil Ciosek, Quan Vuong, Robert Tyler Loftin, Katja Hofmann |
| 2019 | Better Transfer Learning with Inferred Successor Maps. Tamas Madarasz, Tim E. J. Behrens |
| 2019 | Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. Mahesh Chandra Mukkamala, Peter Ochs |
| 2019 | Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs. Marek Petrik, Reazul Hasan Russel |
| 2019 | Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization. Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman |
| 2019 | Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs. Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko |
| 2019 | Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach |
| 2019 | Beyond the Single Neuron Convex Barrier for Neural Network Certification. Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin T. Vechev |
| 2019 | Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon |
| 2019 | Biases for Emergent Communication in Multi-agent Reinforcement Learning. Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel |
| 2019 | Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes. Rishidev Chaudhuri, Ila Fiete |
| 2019 | Blended Matching Pursuit. Cyrille W. Combettes, Sebastian Pokutta |
| 2019 | Blind Super-Resolution Kernel Estimation using an Internal-GAN. Sefi Bell-Kligler, Assaf Shocher, Michal Irani |
| 2019 | Block Coordinate Regularization by Denoising. Yu Sun, Jiaming Liu, Ulugbek Kamilov |
| 2019 | Blocking Bandits. Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai |
| 2019 | Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion. Joan Serrà, Santiago Pascual, Carlos Segura |
| 2019 | Bootstrapping Upper Confidence Bound. Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen, Guang Cheng |
| 2019 | Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo |
| 2019 | Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup |
| 2019 | Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar |
| 2019 | Bridging Machine Learning and Logical Reasoning by Abductive Learning. Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu, Zhi-Hua Zhou |
| 2019 | Budgeted Reinforcement Learning in Continuous State Space. Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin |
| 2019 | CNN Wei-Da Chen, Shan-Hung Wu |
| 2019 | CPM-Nets: Cross Partial Multi-View Networks. Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu |
| 2019 | CXPlain: Causal Explanations for Model Interpretation under Uncertainty. Patrick Schwab, Walter Karlen |
| 2019 | Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization. Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann |
| 2019 | Calibration tests in multi-class classification: A unifying framework. David Widmann, Fredrik Lindsten, Dave Zachariah |
| 2019 | Can SGD Learn Recurrent Neural Networks with Provable Generalization? Zeyuan Allen-Zhu, Yuanzhi Li |
| 2019 | Can Unconditional Language Models Recover Arbitrary Sentences? Nishant Subramani, Samuel R. Bowman, Kyunghyun Cho |
| 2019 | Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado |
| 2019 | Capacity Bounded Differential Privacy. Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala |
| 2019 | Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution. Thang Vu, Hyunjun Jang, Trung X. Pham, Chang Dong Yoo |
| 2019 | Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction. Hao Zheng, Faming Fang, Guixu Zhang |
| 2019 | Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning. Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jianmin Wang |
| 2019 | Categorized Bandits. Matthieu Jedor, Vianney Perchet, Jonathan Louëdec |
| 2019 | Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation. Qiming Zhang, Jing Zhang, Wei Liu, Dacheng Tao |
| 2019 | Causal Confusion in Imitation Learning. Pim de Haan, Dinesh Jayaraman, Sergey Levine |
| 2019 | Causal Regularization. Dominik Janzing |
| 2019 | Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback. Arun Verma, Manjesh Kumar Hanawal, Arun Rajkumar, Raman Sankaran |
| 2019 | Certainty Equivalence is Efficient for Linear Quadratic Control. Horia Mania, Stephen Tu, Benjamin Recht |
| 2019 | Certifiable Robustness to Graph Perturbations. Aleksandar Bojchevski, Stephan Günnemann |
| 2019 | Certified Adversarial Robustness with Additive Noise. Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin |
| 2019 | Certifying Geometric Robustness of Neural Networks. Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin T. Vechev |
| 2019 | Channel Gating Neural Networks. Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh |
| 2019 | Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim |
| 2019 | Characterizing Bias in Classifiers using Generative Models. Daniel McDuff, Shuang Ma, Yale Song, Ashish Kapoor |
| 2019 | Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory. Bin Hu, Usman Ahmed Syed |
| 2019 | Chasing Ghosts: Instruction Following as Bayesian State Tracking. Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee |
| 2019 | Chirality Nets for Human Pose Regression. Raymond A. Yeh, Yuan-Ting Hu, Alexander G. Schwing |
| 2019 | Classification Accuracy Score for Conditional Generative Models. Suman V. Ravuri, Oriol Vinyals |
| 2019 | Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann |
| 2019 | Co-Generation with GANs using AIS based HMC. Tiantian Fang, Alexander G. Schwing |
| 2019 | Coda: An End-to-End Neural Program Decompiler. Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao |
| 2019 | Code Generation as a Dual Task of Code Summarization. Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin |
| 2019 | Cold Case: The Lost MNIST Digits. Chhavi Yadav, Léon Bottou |
| 2019 | Combinatorial Bandits with Relative Feedback. Aadirupa Saha, Aditya Gopalan |
| 2019 | Combinatorial Bayesian Optimization using the Graph Cartesian Product. ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling |
| 2019 | Combinatorial Inference against Label Noise. Paul Hongsuck Seo, Geeho Kim, Bohyung Han |
| 2019 | Combining Generative and Discriminative Models for Hybrid Inference. Victor Garcia Satorras, Max Welling, Zeynep Akata |
| 2019 | Communication trade-offs for Local-SGD with large step size. Aymeric Dieuleveut, Kumar Kshitij Patel |
| 2019 | Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. Shuai Zheng, Ziyue Huang, James T. Kwok |
| 2019 | Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients. Jun Sun, Tianyi Chen, Georgios B. Giannakis, Zaiyue Yang |
| 2019 | Communication-efficient Distributed SGD with Sketching. Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora |
| 2019 | Compacting, Picking and Growing for Unforgetting Continual Learning. Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, Chu-Song Chen |
| 2019 | Comparing Unsupervised Word Translation Methods Step by Step. Mareike Hartmann, Yova Kementchedjhieva, Anders Søgaard |
| 2019 | Comparing distributions: 퓁 Meyer Scetbon, Gaël Varoquaux |
| 2019 | Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex. Jianghong Shi, Eric Shea-Brown, Michael A. Buice |
| 2019 | Competitive Gradient Descent. Florian Schäfer, Anima Anandkumar |
| 2019 | Compiler Auto-Vectorization with Imitation Learning. Charith Mendis, Cambridge Yang, Yewen Pu, Saman P. Amarasinghe, Michael Carbin |
| 2019 | Complexity of Highly Parallel Non-Smooth Convex Optimization. Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford |
| 2019 | Compositional De-Attention Networks. Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui |
| 2019 | Compositional Plan Vectors. Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine |
| 2019 | Compositional generalization through meta sequence-to-sequence learning. Brenden M. Lake |
| 2019 | Compression with Flows via Local Bits-Back Coding. Jonathan Ho, Evan Lohn, Pieter Abbeel |
| 2019 | Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization. Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, Bill Freeman, Frédo Durand |
| 2019 | Computational Separations between Sampling and Optimization. Kunal Talwar |
| 2019 | Computing Full Conformal Prediction Set with Approximate Homotopy. Eugène Ndiaye, Ichiro Takeuchi |
| 2019 | Computing Linear Restrictions of Neural Networks. Matthew Sotoudeh, Aditya V. Thakur |
| 2019 | Concentration of risk measures: A Wasserstein distance approach. Sanjay P. Bhat, Prashanth L. A. |
| 2019 | CondConv: Conditionally Parameterized Convolutions for Efficient Inference. Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam |
| 2019 | Conditional Independence Testing using Generative Adversarial Networks. Alexis Bellot, Mihaela van der Schaar |
| 2019 | Conditional Structure Generation through Graph Variational Generative Adversarial Nets. Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li |
| 2019 | Conformal Prediction Under Covariate Shift. Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas |
| 2019 | Conformalized Quantile Regression. Yaniv Romano, Evan Patterson, Emmanuel J. Candès |
| 2019 | Connections Between Mirror Descent, Thompson Sampling and the Information Ratio. Julian Zimmert, Tor Lattimore |
| 2019 | Connective Cognition Network for Directional Visual Commonsense Reasoning. Aming Wu, Linchao Zhu, Yahong Han, Yi Yang |
| 2019 | Consistency-based Semi-supervised Learning for Object detection. Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak |
| 2019 | Constrained Reinforcement Learning Has Zero Duality Gap. Santiago Paternain, Luiz F. O. Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro |
| 2019 | Constrained deep neural network architecture search for IoT devices accounting for hardware calibration. Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi |
| 2019 | Constraint-based Causal Structure Learning with Consistent Separating Sets. Honghao Li, Vincent Cabeli, Nadir Sella, Hervé Isambert |
| 2019 | Contextual Bandits with Cross-Learning. Santiago R. Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab S. Mirrokni, Jon Schneider |
| 2019 | Continual Unsupervised Representation Learning. Dushyant Rao, Francesco Visin, Andrei A. Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell |
| 2019 | Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh |
| 2019 | Continuous-time Models for Stochastic Optimization Algorithms. Antonio Orvieto, Aurélien Lucchi |
| 2019 | Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. Fengxiang He, Tongliang Liu, Dacheng Tao |
| 2019 | Control What You Can: Intrinsically Motivated Task-Planning Agent. Sebastian Blaes, Marin Vlastelica Pogancic, Jia-Jie Zhu, Georg Martius |
| 2019 | Controllable Text-to-Image Generation. Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr |
| 2019 | Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation. Ke Wang, Hang Hua, Xiaojun Wan |
| 2019 | Controlling Neural Level Sets. Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman |
| 2019 | Convergence Guarantees for Adaptive Bayesian Quadrature Methods. Motonobu Kanagawa, Philipp Hennig |
| 2019 | Convergence of Adversarial Training in Overparametrized Neural Networks. Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason D. Lee |
| 2019 | Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control. Miguel Vaquero, Jorge Cortés |
| 2019 | Convergent Policy Optimization for Safe Reinforcement Learning. Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang |
| 2019 | Convolution with even-sized kernels and symmetric padding. Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi |
| 2019 | Coordinated hippocampal-entorhinal replay as structural inference. Talfan Evans, Neil Burgess |
| 2019 | Copula Multi-label Learning. Weiwei Liu |
| 2019 | Copula-like Variational Inference. Marcel Hirt, Petros Dellaportas, Alain Durmus |
| 2019 | Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. Natasa Tagasovska, Damien Ackerer, Thibault Vatter |
| 2019 | Coresets for Archetypal Analysis. Sebastian Mair, Ulf Brefeld |
| 2019 | Coresets for Clustering with Fairness Constraints. Lingxiao Huang, Shaofeng H.-C. Jiang, Nisheeth K. Vishnoi |
| 2019 | Cormorant: Covariant Molecular Neural Networks. Brandon M. Anderson, Truong-Son Hy, Risi Kondor |
| 2019 | Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels. Natalia Neverova, David Novotný, Andrea Vedaldi |
| 2019 | Correlation Clustering with Adaptive Similarity Queries. Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale |
| 2019 | Correlation Priors for Reinforcement Learning. Bastian Alt, Adrian Sosic, Heinz Koeppl |
| 2019 | Correlation clustering with local objectives. Sanchit Kalhan, Konstantin Makarychev, Timothy Zhou |
| 2019 | Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks. Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm |
| 2019 | Cost Effective Active Search. Shali Jiang, Roman Garnett, Benjamin Moseley |
| 2019 | Counting the Optimal Solutions in Graphical Models. Radu Marinescu, Rina Dechter |
| 2019 | Covariate-Powered Empirical Bayes Estimation. Nikolaos Ignatiadis, Stefan Wager |
| 2019 | Cross Attention Network for Few-shot Classification. Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen |
| 2019 | Cross-Domain Transferability of Adversarial Perturbations. Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, Fatih Porikli |
| 2019 | Cross-Modal Learning with Adversarial Samples. Chao Li, Shangqian Gao, Cheng Deng, De Xie, Wei Liu |
| 2019 | Cross-channel Communication Networks. Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh |
| 2019 | Cross-lingual Language Model Pretraining. Alexis Conneau, Guillaume Lample |
| 2019 | Cross-sectional Learning of Extremal Dependence among Financial Assets. Xing Yan, Qi Wu, Wen Zhang |
| 2019 | Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms. Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang |
| 2019 | Curriculum-guided Hindsight Experience Replay. Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang |
| 2019 | Curvilinear Distance Metric Learning. Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang |
| 2019 | D-VAE: A Variational Autoencoder for Directed Acyclic Graphs. Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen |
| 2019 | DAC: The Double Actor-Critic Architecture for Learning Options. Shangtong Zhang, Shimon Whiteson |
| 2019 | DATA: Differentiable ArchiTecture Approximation. Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan |
| 2019 | DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation. Shashank Rajput, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos |
| 2019 | DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters. Asiri Wijesinghe, Qing Wang |
| 2019 | DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. Rixon Crane, Fred Roosta |
| 2019 | DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction. Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomír Mech, Ulrich Neumann |
| 2019 | DM2C: Deep Mixed-Modal Clustering. Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang |
| 2019 | DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs. Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang |
| 2019 | DTWNet: a Dynamic Time Warping Network. Xingyu Cai, Tingyang Xu, Jinfeng Yi, Junzhou Huang, Sanguthevar Rajasekaran |
| 2019 | Dancing to Music. Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz |
| 2019 | Data Cleansing for Models Trained with SGD. Satoshi Hara, Atsushi Nitanda, Takanori Maehara |
| 2019 | Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum. Shreyas Saxena, Oncel Tuzel, Dennis DeCoste |
| 2019 | Data-Dependence of Plateau Phenomenon in Learning with Neural Network - Statistical Mechanical Analysis. Yuki Yoshida, Masato Okada |
| 2019 | Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation. Colin Wei, Tengyu Ma |
| 2019 | Data-driven Estimation of Sinusoid Frequencies. Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda |
| 2019 | Debiased Bayesian inference for average treatment effects. Kolyan Ray, Botond Szabó |
| 2019 | Decentralized Cooperative Stochastic Bandits. David Martínez-Rubio, Varun Kanade, Patrick Rebeschini |
| 2019 | Decentralized sketching of low rank matrices. Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg |
| 2019 | Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski |
| 2019 | Deep Active Learning with a Neural Architecture Search. Yonatan Geifman, Ran El-Yaniv |
| 2019 | Deep Equilibrium Models. Shaojie Bai, J. Zico Kolter, Vladlen Koltun |
| 2019 | Deep Gamblers: Learning to Abstain with Portfolio Theory. Liu Ziyin, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda |
| 2019 | Deep Generalized Method of Moments for Instrumental Variable Analysis. Andrew Bennett, Nathan Kallus, Tobias Schnabel |
| 2019 | Deep Generative Video Compression. Salvator Lombardo, Jun Han, Christopher Schroers, Stephan Mandt |
| 2019 | Deep Leakage from Gradients. Ligeng Zhu, Zhijian Liu, Song Han |
| 2019 | Deep Learning without Weight Transport. Mohamed Akrout, Collin Wilson, Peter Conway Humphreys, Timothy P. Lillicrap, Douglas B. Tweed |
| 2019 | Deep Model Transferability from Attribution Maps. Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song |
| 2019 | Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces. Benyamin Allahgholizadeh Haghi, Spencer S. Kellis, Sahil Shah, Maitreyi Ashok, Luke Bashford, Daniel Kramer, Brian C. Lee, Charles Liu, Richard A. Andersen, Azita Emami |
| 2019 | Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling. Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao |
| 2019 | Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion. Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann |
| 2019 | Deep Random Splines for Point Process Intensity Estimation of Neural Population Data. Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham |
| 2019 | Deep ReLU Networks Have Surprisingly Few Activation Patterns. Boris Hanin, David Rolnick |
| 2019 | Deep Scale-spaces: Equivariance Over Scale. Daniel E. Worrall, Max Welling |
| 2019 | Deep Set Prediction Networks. Yan Zhang, Jonathon S. Hare, Adam Prügel-Bennett |
| 2019 | Deep Signature Transforms. Patrick Kidger, Patric Bonnier, Imanol Pérez Arribas, Cristopher Salvi, Terry J. Lyons |
| 2019 | Deep Structured Prediction for Facial Landmark Detection. Lisha Chen, Hui Su, Qiang Ji |
| 2019 | Deep Supervised Summarization: Algorithm and Application to Learning Instructions. Chengguang Xu, Ehsan Elhamifar |
| 2019 | Deep imitation learning for molecular inverse problems. Eric Jonas |
| 2019 | DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision. Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox |
| 2019 | DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging. Matthieu Simeoni, Sepand Kashani, Paul Hurley, Martin Vetterli |
| 2019 | Defending Against Neural Fake News. Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi |
| 2019 | Defending Neural Backdoors via Generative Distribution Modeling. Ximing Qiao, Yukun Yang, Hai Li |
| 2019 | Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training. Haichao Zhang, Jianyu Wang |
| 2019 | Deliberative Explanations: visualizing network insecurities. Pei Wang, Nuno Vasconcelos |
| 2019 | Demystifying Black-box Models with Symbolic Metamodels. Ahmed M. Alaa, Mihaela van der Schaar |
| 2019 | Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning. Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea |
| 2019 | DetNAS: Backbone Search for Object Detection. Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian Sun |
| 2019 | Detecting Overfitting via Adversarial Examples. Roman Werpachowski, András György, Csaba Szepesvári |
| 2019 | Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, Yang Liu |
| 2019 | Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette |
| 2019 | Diffeomorphic Temporal Alignment Nets. Ron Shapira Weber, Matan Eyal, Nicki Skafte Detlefsen, Oren Shriki, Oren Freifeld |
| 2019 | Differentiable Cloth Simulation for Inverse Problems. Junbang Liang, Ming C. Lin, Vladlen Koltun |
| 2019 | Differentiable Convex Optimization Layers. Akshay Agrawal, Brandon Amos, Shane T. Barratt, Stephen P. Boyd, Steven Diamond, J. Zico Kolter |
| 2019 | Differentiable Ranking and Sorting using Optimal Transport. Marco Cuturi, Olivier Teboul, Jean-Philippe Vert |
| 2019 | Differential Privacy Has Disparate Impact on Model Accuracy. Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov |
| 2019 | Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman |
| 2019 | Differentially Private Anonymized Histograms. Ananda Theertha Suresh |
| 2019 | Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. James Jordon, Jinsung Yoon, Mihaela van der Schaar |
| 2019 | Differentially Private Bayesian Linear Regression. Garrett Bernstein, Daniel Sheldon |
| 2019 | Differentially Private Covariance Estimation. Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii |
| 2019 | Differentially Private Distributed Data Summarization under Covariate Shift. Kanthi K. Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculín |
| 2019 | Differentially Private Markov Chain Monte Carlo. Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela |
| 2019 | Diffusion Improves Graph Learning. Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann |
| 2019 | Dimension-Free Bounds for Low-Precision Training. Zheng Li, Christopher De Sa |
| 2019 | Dimensionality reduction: theoretical perspective on practical measures. Yair Bartal, Nova Fandina, Ofer Neiman |
| 2019 | Direct Estimation of Differential Functional Graphical Models. Boxin Zhao, Y. Samuel Wang, Mladen Kolar |
| 2019 | Direct Optimization through arg max for Discrete Variational Auto-Encoder. Guy Lorberbom, Tommi S. Jaakkola, Andreea Gane, Tamir Hazan |
| 2019 | Discovering Neural Wirings. Mitchell Wortsman, Ali Farhadi, Mohammad Rastegari |
| 2019 | Discovery of Useful Questions as Auxiliary Tasks. Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh |
| 2019 | Discrete Flows: Invertible Generative Models of Discrete Data. Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole |
| 2019 | Discrete Object Generation with Reversible Inductive Construction. Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams |
| 2019 | Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design. Faidra Georgia Monachou, Itai Ashlagi |
| 2019 | Discriminative Topic Modeling with Logistic LDA. Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis |
| 2019 | Discriminator optimal transport. Akinori Tanaka |
| 2019 | Disentangled behavioural representations. Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong |
| 2019 | Disentangling Influence: Using disentangled representations to audit model predictions. Charles T. Marx, Richard L. Phillips, Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian |
| 2019 | Distinguishing Distributions When Samples Are Strategically Transformed. Hanrui Zhang, Yu Cheng, Vincent Conitzer |
| 2019 | Distributed Low-rank Matrix Factorization With Exact Consensus. Zhihui Zhu, Qiuwei Li, Xinshuo Yang, Gongguo Tang, Michael B. Wakin |
| 2019 | Distributed estimation of the inverse Hessian by determinantal averaging. Michal Derezinski, Michael W. Mahoney |
| 2019 | Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor. Meera Pai, Animesh Kumar |
| 2019 | Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards. Anmol Kagrecha, Jayakrishnan Nair, Krishna P. Jagannathan |
| 2019 | Distribution-Independent PAC Learning of Halfspaces with Massart Noise. Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos |
| 2019 | Distributional Policy Optimization: An Alternative Approach for Continuous Control. Chen Tessler, Guy Tennenholtz, Shie Mannor |
| 2019 | Distributional Reward Decomposition for Reinforcement Learning. Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-Yan Liu, Guangwen Yang |
| 2019 | Distributionally Robust Optimization and Generalization in Kernel Methods. Matthew Staib, Stefanie Jegelka |
| 2019 | Divergence-Augmented Policy Optimization. Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang |
| 2019 | Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. Justin Domke, Daniel Sheldon |
| 2019 | Domain Generalization via Model-Agnostic Learning of Semantic Features. Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, Ben Glocker |
| 2019 | Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction. Aleksis Pirinen, Erik Gärtner, Cristian Sminchisescu |
| 2019 | Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi |
| 2019 | Don't take it lightly: Phasing optical random projections with unknown operators. Sidharth Gupta, Rémi Gribonval, Laurent Daudet, Ivan Dokmanic |
| 2019 | Double Quantization for Communication-Efficient Distributed Optimization. Yue Yu, Jiaxiang Wu, Longbo Huang |
| 2019 | Doubly-Robust Lasso Bandit. Gi-Soo Kim, Myunghee Cho Paik |
| 2019 | DppNet: Approximating Determinantal Point Processes with Deep Networks. Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek |
| 2019 | Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries. Fuwen Tan, Paola Cascante-Bonilla, Xiaoxiao Guo, Hui Wu, Song Feng, Vicente Ordonez |
| 2019 | Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning. Jian Ni, Shanghang Zhang, Haiyong Xie |
| 2019 | Dual Variational Generation for Low Shot Heterogeneous Face Recognition. Chaoyou Fu, Xiang Wu, Yibo Hu, Huaibo Huang, Ran He |
| 2019 | DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li |
| 2019 | Dying Experts: Efficient Algorithms with Optimal Regret Bounds. Hamid Shayestehmanesh, Sajjad Azami, Nishant A. Mehta |
| 2019 | Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces. Yu Qi, Bin Liu, Yueming Wang, Gang Pan |
| 2019 | Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions. Negin Golrezaei, Adel Javanmard, Vahab S. Mirrokni |
| 2019 | Dynamic Local Regret for Non-convex Online Forecasting. Sergül Aydöre, Tianhao Zhu, Dean P. Foster |
| 2019 | Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová |
| 2019 | E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings. Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang |
| 2019 | ETNet: Error Transition Network for Arbitrary Style Transfer. Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang |
| 2019 | Ease-of-Teaching and Language Structure from Emergent Communication. Fushan Li, Michael Bowling |
| 2019 | Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, Marius Kloft |
| 2019 | Efficient Algorithms for Smooth Minimax Optimization. Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh |
| 2019 | Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao |
| 2019 | Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control. Sai Qian Zhang, Qi Zhang, Jieyu Lin |
| 2019 | Efficient Convex Relaxations for Streaming PCA. Raman Arora, Teodor Vanislavov Marinov |
| 2019 | Efficient Deep Approximation of GMMs. Shirin Jalali, Carl J. Nuzman, Iraj Saniee |
| 2019 | Efficient Forward Architecture Search. Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey |
| 2019 | Efficient Graph Generation with Graph Recurrent Attention Networks. Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel |
| 2019 | Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. Daniel Kumor, Bryant Chen, Elias Bareinboim |
| 2019 | Efficient Meta Learning via Minibatch Proximal Update. Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng |
| 2019 | Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models. Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama |
| 2019 | Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection. Junran Peng, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie Yan |
| 2019 | Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood |
| 2019 | Efficient Pure Exploration in Adaptive Round model. Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen |
| 2019 | Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium. Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm |
| 2019 | Efficient Rematerialization for Deep Networks. Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang |
| 2019 | Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. Huizhuo Yuan, Xiangru Lian, Chris Junchi Li, Ji Liu, Wenqing Hu |
| 2019 | Efficient Symmetric Norm Regression via Linear Sketching. Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong |
| 2019 | Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks. Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, George J. Pappas |
| 2019 | Efficient and Thrifty Voting by Any Means Necessary. Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David P. Woodruff |
| 2019 | Efficient characterization of electrically evoked responses for neural interfaces. Nishal P. Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan M. Litke, Liam Paninski, E. J. Chichilnisky |
| 2019 | Efficient online learning with kernels for adversarial large scale problems. Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi |
| 2019 | Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. Jonathan R. Ullman, Adam Sealfon |
| 2019 | Efficiently Learning Fourier Sparse Set Functions. Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause |
| 2019 | Efficiently avoiding saddle points with zero order methods: No gradients required. Emmanouil V. Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras |
| 2019 | Efficiently escaping saddle points on manifolds. Chris Criscitiello, Nicolas Boumal |
| 2019 | Elliptical Perturbations for Differential Privacy. Matthew Reimherr, Jordan Awan |
| 2019 | Embedding Symbolic Knowledge into Deep Networks. Yaqi Xie, Ziwei Xu, Kuldeep S. Meel, Mohan S. Kankanhalli, Harold Soh |
| 2019 | Emergence of Object Segmentation in Perturbed Generative Models. Adam Bielski, Paolo Favaro |
| 2019 | Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness. Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans |
| 2019 | Enabling hyperparameter optimization in sequential autoencoders for spiking neural data. Mohammad Reza Keshtkaran, Chethan Pandarinath |
| 2019 | End to end learning and optimization on graphs. Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe |
| 2019 | End-to-End Learning on 3D Protein Structure for Interface Prediction. Raphael J. L. Townshend, Rishi Bedi, Patricia Suriana, Ron O. Dror |
| 2019 | Energy-Inspired Models: Learning with Sampler-Induced Distributions. Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath |
| 2019 | Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan |
| 2019 | Envy-Free Classification. Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia |
| 2019 | Episodic Memory in Lifelong Language Learning. Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama |
| 2019 | Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits. Sivan Sabato |
| 2019 | Equal Opportunity in Online Classification with Partial Feedback. Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu |
| 2019 | Equipping Experts/Bandits with Long-term Memory. Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang |
| 2019 | Equitable Stable Matchings in Quadratic Time. Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras |
| 2019 | Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks. Gunjan Verma, Ananthram Swami |
| 2019 | Escaping from saddle points on Riemannian manifolds. Yue Sun, Nicolas Flammarion, Maryam Fazel |
| 2019 | Estimating Convergence of Markov chains with L-Lag Couplings. Niloy Biswas, Pierre E. Jacob, Paul Vanetti |
| 2019 | Estimating Entropy of Distributions in Constant Space. Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun |
| 2019 | Evaluating Protein Transfer Learning with TAPE. Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John F. Canny, Pieter Abbeel, Yun S. Song |
| 2019 | Exact Combinatorial Optimization with Graph Convolutional Neural Networks. Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi |
| 2019 | Exact Gaussian Processes on a Million Data Points. Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson |
| 2019 | Exact Rate-Distortion in Autoencoders via Echo Noise. Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg |
| 2019 | Exact inference in structured prediction. Kevin Bello, Jean Honorio |
| 2019 | Exact sampling of determinantal point processes with sublinear time preprocessing. Michal Derezinski, Daniele Calandriello, Michal Valko |
| 2019 | Experience Replay for Continual Learning. David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne |
| 2019 | Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora |
| 2019 | Explanations can be manipulated and geometry is to blame. Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel |
| 2019 | Explicit Disentanglement of Appearance and Perspective in Generative Models. Nicki Skafte Detlefsen, Søren Hauberg |
| 2019 | Explicit Explore-Exploit Algorithms in Continuous State Spaces. Mikael Henaff |
| 2019 | Explicit Planning for Efficient Exploration in Reinforcement Learning. Liangpeng Zhang, Ke Tang, Xin Yao |
| 2019 | Explicitly disentangling image content from translation and rotation with spatial-VAE. Tristan Bepler, Ellen D. Zhong, Kotaro Kelley, Edward Brignole, Bonnie Berger |
| 2019 | Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations. Xu Wang, Jingming He, Lin Ma |
| 2019 | Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric |
| 2019 | Exploration via Hindsight Goal Generation. Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng |
| 2019 | Exploring Algorithmic Fairness in Robust Graph Covering Problems. Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder, Amulya Yadav, Milind Tambe |
| 2019 | Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks. Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda |
| 2019 | Exponential Family Estimation via Adversarial Dynamics Embedding. Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans |
| 2019 | Exponentially convergent stochastic k-PCA without variance reduction. Cheng Tang |
| 2019 | Expressive power of tensor-network factorizations for probabilistic modeling. Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac |
| 2019 | Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images. Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun |
| 2019 | Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products. Tharun Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava |
| 2019 | Face Reconstruction from Voice using Generative Adversarial Networks. Yandong Wen, Bhiksha Raj, Rita Singh |
| 2019 | Facility Location Problem in Differential Privacy Model Revisited. Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang |
| 2019 | Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery. Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell |
| 2019 | Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton |
| 2019 | Fair Algorithms for Clustering. Suman Kalyan Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani |
| 2019 | Fast AutoAugment. Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim |
| 2019 | Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. Frederic Koehler |
| 2019 | Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. Guodong Zhang, James Martens, Roger B. Grosse |
| 2019 | Fast Decomposable Submodular Function Minimization using Constrained Total Variation. Senanayak Sesh Kumar Karri, Francis R. Bach, Thomas Pock |
| 2019 | Fast Efficient Hyperparameter Tuning for Policy Gradient Methods. Supratik Paul, Vitaly Kurin, Shimon Whiteson |
| 2019 | Fast Low-rank Metric Learning for Large-scale and High-dimensional Data. Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu |
| 2019 | Fast Parallel Algorithms for Statistical Subset Selection Problems. Sharon Qian, Yaron Singer |
| 2019 | Fast Sparse Group Lasso. Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima |
| 2019 | Fast Structured Decoding for Sequence Models. Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhi-Hong Deng |
| 2019 | Fast and Accurate Least-Mean-Squares Solvers. Alaa Maalouf, Ibrahim Jubran, Dan Feldman |
| 2019 | Fast and Accurate Stochastic Gradient Estimation. Beidi Chen, Yingchen Xu, Anshumali Shrivastava |
| 2019 | Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes. James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner |
| 2019 | Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes. James P. Bailey, Georgios Piliouras |
| 2019 | Fast and Provable ADMM for Learning with Generative Priors. Fabian Latorre, Armin Eftekhari, Volkan Cevher |
| 2019 | Fast structure learning with modular regularization. Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan |
| 2019 | Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. Deeksha Adil, Richard Peng, Sushant Sachdeva |
| 2019 | Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. Jun Yang, Shengyang Sun, Daniel M. Roy |
| 2019 | FastSpeech: Fast, Robust and Controllable Text to Speech. Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu |
| 2019 | Faster Boosting with Smaller Memory. Julaiti Alafate, Yoav Freund |
| 2019 | Faster width-dependent algorithm for mixed packing and covering LPs. Digvijay Boob, Saurabh Sawlani, Di Wang |
| 2019 | Few-shot Video-to-Video Synthesis. Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Bryan Catanzaro, Jan Kautz |
| 2019 | Finding Friend and Foe in Multi-Agent Games. Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum |
| 2019 | Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna |
| 2019 | Fine-grained Optimization of Deep Neural Networks. Mete Ozay |
| 2019 | Finite-Sample Analysis for SARSA with Linear Function Approximation. Shaofeng Zou, Tengyu Xu, Yingbin Liang |
| 2019 | Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning. Harsh Gupta, R. Srikant, Lei Ying |
| 2019 | Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. Karl Krauth, Stephen Tu, Benjamin Recht |
| 2019 | First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard |
| 2019 | First Order Motion Model for Image Animation. Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe |
| 2019 | First order expansion of convex regularized estimators. Pierre C. Bellec, Arun K. Kuchibhotla |
| 2019 | First-order methods almost always avoid saddle points: The case of vanishing step-sizes. Ioannis Panageas, Georgios Piliouras, Xiao Wang |
| 2019 | Fisher Efficient Inference of Intractable Models. Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen |
| 2019 | Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions. Chris Russell, Matteo Toso, Neill D. F. Campbell |
| 2019 | Fixing the train-test resolution discrepancy. Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Hervé Jégou |
| 2019 | Flattening a Hierarchical Clustering through Active Learning. Fabio Vitale, Anand Rajagopalan, Claudio Gentile |
| 2019 | Flexible Modeling of Diversity with Strongly Log-Concave Distributions. Joshua Robinson, Suvrit Sra, Stefanie Jegelka |
| 2019 | Flexible information routing in neural populations through stochastic comodulation. Caroline Haimerl, Cristina Savin, Eero P. Simoncelli |
| 2019 | Flow-based Image-to-Image Translation with Feature Disentanglement. Ruho Kondo, Keisuke Kawano, Satoshi Koide, Takuro Kutsuna |
| 2019 | Focused Quantization for Sparse CNNs. Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu |
| 2019 | Fooling Neural Network Interpretations via Adversarial Model Manipulation. Juyeon Heo, Sunghwan Joo, Taesup Moon |
| 2019 | Foundations of Comparison-Based Hierarchical Clustering. Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg |
| 2019 | FreeAnchor: Learning to Match Anchors for Visual Object Detection. Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye |
| 2019 | From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani |
| 2019 | From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli |
| 2019 | From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI. Roman Beliy, Guy Gaziv, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani |
| 2019 | Full-Gradient Representation for Neural Network Visualization. Suraj Srinivas, François Fleuret |
| 2019 | Fully Dynamic Consistent Facility Location. Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn |
| 2019 | Fully Neural Network based Model for General Temporal Point Processes. Takahiro Omi, Naonori Ueda, Kazuyuki Aihara |
| 2019 | Fully Parameterized Quantile Function for Distributional Reinforcement Learning. Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu |
| 2019 | Function-Space Distributions over Kernels. Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson |
| 2019 | Functional Adversarial Attacks. Cassidy Laidlaw, Soheil Feizi |
| 2019 | G2SAT: Learning to Generate SAT Formulas. Jiaxuan You, Haoze Wu, Clark W. Barrett, Raghuram Ramanujan, Jure Leskovec |
| 2019 | GENO - GENeric Optimization for Classical Machine Learning. Sören Laue, Matthias Mitterreiter, Joachim Giesen |
| 2019 | GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs. Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, Xiaowei Zhou |
| 2019 | GNNExplainer: Generating Explanations for Graph Neural Networks. Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec |
| 2019 | GOT: An Optimal Transport framework for Graph comparison. Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard |
| 2019 | GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen |
| 2019 | GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series. Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau |
| 2019 | Game Design for Eliciting Distinguishable Behavior. Fan Yang, Liu Leqi, Yifan Wu, Zachary Chase Lipton, Pradeep Ravikumar, Tom M. Mitchell, William W. Cohen |
| 2019 | Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks. Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, Ping Wang |
| 2019 | Gaussian-Based Pooling for Convolutional Neural Networks. Takumi Kobayashi |
| 2019 | General E(2)-Equivariant Steerable CNNs. Maurice Weiler, Gabriele Cesa |
| 2019 | General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme. Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao |
| 2019 | Generalization Bounds for Neural Networks via Approximate Description Length. Amit Daniely, Elad Granot |
| 2019 | Generalization Bounds in the Predict-then-Optimize Framework. Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari |
| 2019 | Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. Yuan Cao, Quanquan Gu |
| 2019 | Generalization Error Analysis of Quantized Compressive Learning. Xiaoyun Li, Ping Li |
| 2019 | Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection. Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou |
| 2019 | Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann |
| 2019 | Generalization in multitask deep neural classifiers: a statistical physics approach. Anthony Ndirango, Tyler Lee |
| 2019 | Generalization of Reinforcement Learners with Working and Episodic Memory. Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià Puigdomènech Badia, Gavin Buttimore, Charlie Deck, Joel Z. Leibo, Charles Blundell |
| 2019 | Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer. Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang |
| 2019 | Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. Pedro Mercado, Francesco Tudisco, Matthias Hein |
| 2019 | Generalized Off-Policy Actor-Critic. Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson |
| 2019 | Generalized Sliced Wasserstein Distances. Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde |
| 2019 | Generating Diverse High-Fidelity Images with VQ-VAE-2. Ali Razavi, Aäron van den Oord, Oriol Vinyals |
| 2019 | Generative Modeling by Estimating Gradients of the Data Distribution. Yang Song, Stefano Ermon |
| 2019 | Generative Models for Graph-Based Protein Design. John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi S. Jaakkola |
| 2019 | Generative Well-intentioned Networks. Justin Cosentino, Jun Zhu |
| 2019 | Geometry-Aware Neural Rendering. Joshua Tobin, Wojciech Zaremba, Pieter Abbeel |
| 2019 | Global Convergence of Gradient Descent for Deep Linear Residual Networks. Lei Wu, Qingcan Wang, Chao Ma |
| 2019 | Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities. Wei Qian, Yuqian Zhang, Yudong Chen |
| 2019 | Global Guarantees for Blind Demodulation with Generative Priors. Paul Hand, Babhru Joshi |
| 2019 | Global Sparse Momentum SGD for Pruning Very Deep Neural Networks. Xiaohan Ding, Guiguang Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu |
| 2019 | Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses. Ulysse Marteau-Ferey, Francis R. Bach, Alessandro Rudi |
| 2019 | Globally Optimal Learning for Structured Elliptical Losses. Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel |
| 2019 | Globally optimal score-based learning of directed acyclic graphs in high-dimensions. Bryon Aragam, Arash A. Amini, Qing Zhou |
| 2019 | Glyce: Glyph-vectors for Chinese Character Representations. Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li |
| 2019 | Goal-conditioned Imitation Learning. Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp |
| 2019 | Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat |
| 2019 | Gradient Dynamics of Shallow Univariate ReLU Networks. Francis Williams, Matthew Trager, Daniele Panozzo, Cláudio T. Silva, Denis Zorin, Joan Bruna |
| 2019 | Gradient Information for Representation and Modeling. Jie Ding, A. Robert Calderbank, Vahid Tarokh |
| 2019 | Gradient based sample selection for online continual learning. Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio |
| 2019 | Gradient-based Adaptive Markov Chain Monte Carlo. Michalis K. Titsias, Petros Dellaportas |
| 2019 | Graph Agreement Models for Semi-Supervised Learning. Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil A. Platanios, Sujith Ravi, Andrew Tomkins |
| 2019 | Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. Simon S. Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabás Póczos, Ruosong Wang, Keyulu Xu |
| 2019 | Graph Normalizing Flows. Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky |
| 2019 | Graph Structured Prediction Energy Networks. Colin Graber, Alexander G. Schwing |
| 2019 | Graph Transformer Networks. Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim |
| 2019 | Graph-Based Semi-Supervised Learning with Non-ignorable Non-response. Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Jieping Ye |
| 2019 | Graph-based Discriminators: Sample Complexity and Expressiveness. Roi Livni, Yishay Mansour |
| 2019 | Greedy Sampling for Approximate Clustering in the Presence of Outliers. Aditya Bhaskara, Sharvaree Vadgama, Hong Xu |
| 2019 | Grid Saliency for Context Explanations of Semantic Segmentation. Lukas Hoyer, Mauricio Munoz, Prateek Katiyar, Anna Khoreva, Volker Fischer |
| 2019 | Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness. Xueru Zhang, Mohammadmahdi Khaliligarekani, Cem Tekin, Mingyan Liu |
| 2019 | Guided Meta-Policy Search. Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn |
| 2019 | Guided Similarity Separation for Image Retrieval. Chundi Liu, Guangwei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti |
| 2019 | HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models. Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein |
| 2019 | Hamiltonian Neural Networks. Samuel Greydanus, Misko Dzamba, Jason Yosinski |
| 2019 | Hamiltonian descent for composite objectives. Brendan O'Donoghue, Chris J. Maddison |
| 2019 | Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon |
| 2019 | Heterogeneous Graph Learning for Visual Commonsense Reasoning. Weijiang Yu, Jingwen Zhou, Weihao Yu, Xiaodan Liang, Nong Xiao |
| 2019 | Hierarchical Decision Making by Generating and Following Natural Language Instructions. Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis |
| 2019 | Hierarchical Optimal Transport for Document Representation. Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon |
| 2019 | Hierarchical Optimal Transport for Multimodal Distribution Alignment. John Lee, Max Dabagia, Eva L. Dyer, Christopher Rozell |
| 2019 | Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards. Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang |
| 2019 | High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks. Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee |
| 2019 | High-Dimensional Optimization in Adaptive Random Subspaces. Jonathan Lacotte, Mert Pilanci, Marco Pavone |
| 2019 | High-Quality Self-Supervised Deep Image Denoising. Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila |
| 2019 | High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus |
| 2019 | Hindsight Credit Assignment. Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos |
| 2019 | How degenerate is the parametrization of neural networks with the ReLU activation function? Dennis Elbrächter, Julius Berner, Philipp Grohs |
| 2019 | How to Initialize your Network? Robust Initialization for WeightNorm & ResNets. Devansh Arpit, Víctor Campos, Yoshua Bengio |
| 2019 | Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks. Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan |
| 2019 | Hyper-Graph-Network Decoders for Block Codes. Eliya Nachmani, Lior Wolf |
| 2019 | HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha P. Talukdar |
| 2019 | Hyperbolic Graph Convolutional Neural Networks. Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec |
| 2019 | Hyperbolic Graph Neural Networks. Qi Liu, Maximilian Nickel, Douwe Kiela |
| 2019 | Hyperparameter Learning via Distributional Transfer. Ho Chung Leon Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic |
| 2019 | Hyperspherical Prototype Networks. Pascal Mettes, Elise van der Pol, Cees Snoek |
| 2019 | Hypothesis Set Stability and Generalization. Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan |
| 2019 | Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang |
| 2019 | Identification of Conditional Causal Effects under Markov Equivalence. Amin Jaber, Jiji Zhang, Elias Bareinboim |
| 2019 | Identifying Causal Effects via Context-specific Independence Relations. Santtu Tikka, Antti Hyttinen, Juha Karvanen |
| 2019 | Image Captioning: Transforming Objects into Words. Simao Herdade, Armin Kappeler, Kofi Boakye, Joao Soares |
| 2019 | Image Synthesis with a Single (Robust) Classifier. Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry |
| 2019 | Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement. Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan |
| 2019 | Imitation-Projected Programmatic Reinforcement Learning. Abhinav Verma, Hoang Minh Le, Yisong Yue, Swarat Chaudhuri |
| 2019 | Implicit Generation and Modeling with Energy Based Models. Yilun Du, Igor Mordatch |
| 2019 | Implicit Posterior Variational Inference for Deep Gaussian Processes. Haibin Yu, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai |
| 2019 | Implicit Regularization for Optimal Sparse Recovery. Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini |
| 2019 | Implicit Regularization in Deep Matrix Factorization. Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo |
| 2019 | Implicit Regularization of Accelerated Methods in Hilbert Spaces. Nicolò Pagliana, Lorenzo Rosasco |
| 2019 | Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks. Gauthier Gidel, Francis R. Bach, Simon Lacoste-Julien |
| 2019 | Implicit Semantic Data Augmentation for Deep Networks. Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, Cheng Wu |
| 2019 | Implicitly learning to reason in first-order logic. Vaishak Belle, Brendan Juba |
| 2019 | Importance Resampling for Off-policy Prediction. Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White |
| 2019 | Importance Weighted Hierarchical Variational Inference. Artem Sobolev, Dmitry P. Vetrov |
| 2019 | Improved Precision and Recall Metric for Assessing Generative Models. Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila |
| 2019 | Improved Regret Bounds for Bandit Combinatorial Optimization. Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi |
| 2019 | Improving Black-box Adversarial Attacks with a Transfer-based Prior. Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu |
| 2019 | Improving Textual Network Learning with Variational Homophilic Embeddings. Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin |
| 2019 | In-Place Zero-Space Memory Protection for CNN. Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, Seung-Hwan Lim |
| 2019 | Incremental Few-Shot Learning with Attention Attractor Networks. Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel |
| 2019 | Incremental Scene Synthesis. Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, Yingli Tian, Jan Ernst, Andreas Hutter |
| 2019 | Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits. Yogev Bar-On, Yishay Mansour |
| 2019 | Inducing brain-relevant bias in natural language processing models. Dan Schwartz, Mariya Toneva, Leila Wehbe |
| 2019 | Information Competing Process for Learning Diversified Representations. Jie Hu, Rongrong Ji, Shengchuan Zhang, Xiaoshuai Sun, Qixiang Ye, Chia-Wen Lin, Qi Tian |
| 2019 | Information-Theoretic Confidence Bounds for Reinforcement Learning. Xiuyuan Lu, Benjamin Van Roy |
| 2019 | Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy |
| 2019 | Infra-slow brain dynamics as a marker for cognitive function and decline. Shagun Ajmera, Shreya Rajagopal, Razi Rehman, Devarajan Sridharan |
| 2019 | Inherent Tradeoffs in Learning Fair Representations. Han Zhao, Geoffrey J. Gordon |
| 2019 | Inherent Weight Normalization in Stochastic Neural Networks. Georgios Detorakis, Sourav Dutta, Abhishek Khanna, Matthew Jerry, Suman Datta, Emre Neftci |
| 2019 | Initialization of ReLUs for Dynamical Isometry. Rebekka Burkholz, Alina Dubatovka |
| 2019 | Input Similarity from the Neural Network Perspective. Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka |
| 2019 | Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks. Aya Abdelsalam Ismail, Mohamed K. Gunady, Luiz Pessoa, Héctor Corrada Bravo, Soheil Feizi |
| 2019 | Input-Output Equivalence of Unitary and Contractive RNNs. Melikasadat Emami, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Alyson K. Fletcher |
| 2019 | Integer Discrete Flows and Lossless Compression. Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg, Max Welling |
| 2019 | Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning. Weishi Shi, Qi Yu |
| 2019 | Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems. Robert Osazuwa Ness, Kaushal Paneri, Olga Vitek |
| 2019 | Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. Shuo Yang, Yanyao Shen, Sujay Sanghavi |
| 2019 | Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem. Dongdong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye |
| 2019 | Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time. Alan Kuhnle |
| 2019 | Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). Mariya Toneva, Leila Wehbe |
| 2019 | Interval timing in deep reinforcement learning agents. Ben Deverett, Ryan Faulkner, Meire Fortunato, Gregory Wayne, Joel Z. Leibo |
| 2019 | Intrinsic dimension of data representations in deep neural networks. Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan |
| 2019 | Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. Nathan Kallus, Masatoshi Uehara |
| 2019 | Invariance and identifiability issues for word embeddings. Rachel Carrington, Karthik Bharath, Simon Preston |
| 2019 | Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness. Fanny Yang, Zuowen Wang, Christina Heinze-Deml |
| 2019 | Invert to Learn to Invert. Patrick Putzky, Max Welling |
| 2019 | Invertible Convolutional Flow. Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth |
| 2019 | Inverting Deep Generative models, One layer at a time. Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis |
| 2019 | Is Deeper Better only when Shallow is Good? Eran Malach, Shai Shalev-Shwartz |
| 2019 | Iterative Least Trimmed Squares for Mixed Linear Regression. Yanyao Shen, Sujay Sanghavi |
| 2019 | Joint Optimization of Tree-based Index and Deep Model for Recommender Systems. Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai |
| 2019 | Joint-task Self-supervised Learning for Temporal Correspondence. Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, Ming-Hsuan Yang |
| 2019 | KNG: The K-Norm Gradient Mechanism. Matthew Reimherr, Jordan Awan |
| 2019 | Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights. Maria Jahja, David C. Farrow, Roni Rosenfeld, Ryan J. Tibshirani |
| 2019 | Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards. Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher |
| 2019 | KerGM: Kernelized Graph Matching. Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, Arye Nehorai |
| 2019 | Kernel Instrumental Variable Regression. Rahul Singh, Maneesh Sahani, Arthur Gretton |
| 2019 | Kernel Stein Tests for Multiple Model Comparison. Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum |
| 2019 | Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona |
| 2019 | Kernel quadrature with DPPs. Ayoub Belhadji, Rémi Bardenet, Pierre Chainais |
| 2019 | Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods. Kevin J. Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin |
| 2019 | Kernelized Bayesian Softmax for Text Generation. Ning Miao, Hao Zhou, Chengqi Zhao, Wenxian Shi, Lei Li |
| 2019 | Knowledge Extraction with No Observable Data. Jaemin Yoo, Minyong Cho, Taebum Kim, U Kang |
| 2019 | LCA: Loss Change Allocation for Neural Network Training. Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski |
| 2019 | LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning. Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, Dacheng Tao |
| 2019 | L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise. Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang |
| 2019 | Landmark Ordinal Embedding. Nikhil Ghosh, Yuxin Chen, Yisong Yue |
| 2019 | Language as an Abstraction for Hierarchical Deep Reinforcement Learning. Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn |
| 2019 | Large Memory Layers with Product Keys. Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou |
| 2019 | Large Scale Adversarial Representation Learning. Jeff Donahue, Karen Simonyan |
| 2019 | Large Scale Markov Decision Processes with Changing Rewards. Adrian Rivera Cardoso, He Wang, Huan Xu |
| 2019 | Large Scale Structure of Neural Network Loss Landscapes. Stanislav Fort, Stanislaw Jastrzebski |
| 2019 | Large-scale optimal transport map estimation using projection pursuit. Cheng Meng, Yuan Ke, Jingyi Zhang, Mengrui Zhang, Wenxuan Zhong, Ping Ma |
| 2019 | Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. Yulia Rubanova, Tian Qi Chen, David Duvenaud |
| 2019 | Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization. Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder |
| 2019 | Latent distance estimation for random geometric graphs. Ernesto Araya Valdivia, Yohann de Castro |
| 2019 | Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu |
| 2019 | Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models. Yunfei Teng, Wenbo Gao, François Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller |
| 2019 | Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge. Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao |
| 2019 | Learnable Tree Filter for Structure-preserving Feature Transform. Lin Song, Yanwei Li, Zeming Li, Gang Yu, Hongbin Sun, Jian Sun, Nanning Zheng |
| 2019 | Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla |
| 2019 | Learning Auctions with Robust Incentive Guarantees. Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern |
| 2019 | Learning Bayesian Networks with Low Rank Conditional Probability Tables. Adarsh Barik, Jean Honorio |
| 2019 | Learning Compositional Neural Programs with Recursive Tree Search and Planning. Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas |
| 2019 | Learning Conditional Deformable Templates with Convolutional Networks. Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu |
| 2019 | Learning Data Manipulation for Augmentation and Weighting. Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom M. Mitchell, Eric P. Xing |
| 2019 | Learning Deep Bilinear Transformation for Fine-grained Image Representation. Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo |
| 2019 | Learning Deterministic Weighted Automata with Queries and Counterexamples. Gail Weiss, Yoav Goldberg, Eran Yahav |
| 2019 | Learning Disentangled Representation for Robust Person Re-identification. Chanho Eom, Bumsub Ham |
| 2019 | Learning Disentangled Representations for Recommendation. Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu |
| 2019 | Learning Distributions Generated by One-Layer ReLU Networks. Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi |
| 2019 | Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning. Wonjae Kim, Yoonho Lee |
| 2019 | Learning Erdos-Renyi Random Graphs via Edge Detecting Queries. Zihan Li, Matthias Fresacher, Jonathan Scarlett |
| 2019 | Learning Fairness in Multi-Agent Systems. Jiechuan Jiang, Zongqing Lu |
| 2019 | Learning GANs and Ensembles Using Discrepancy. Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang |
| 2019 | Learning Generalizable Device Placement Algorithms for Distributed Machine Learning. Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh |
| 2019 | Learning Hawkes Processes from a handful of events. Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran |
| 2019 | Learning Hierarchical Priors in VAEs. Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt |
| 2019 | Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma |
| 2019 | Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling. Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen |
| 2019 | Learning Local Search Heuristics for Boolean Satisfiability. Emre Yolcu, Barnabás Póczos |
| 2019 | Learning Macroscopic Brain Connectomes via Group-Sparse Factorization. Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White |
| 2019 | Learning Mean-Field Games. Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang |
| 2019 | Learning Mixtures of Plackett-Luce Models from Structured Partial Orders. Zhibing Zhao, Lirong Xia |
| 2019 | Learning Multiple Markov Chains via Adaptive Allocation. Mohammad Sadegh Talebi, Odalric-Ambrym Maillard |
| 2019 | Learning Nearest Neighbor Graphs from Noisy Distance Samples. Blake Mason, Ardhendu Tripathy, Robert D. Nowak |
| 2019 | Learning Neural Networks with Adaptive Regularization. Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon |
| 2019 | Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks. Joshua K. Lee, Prasanna Sattigeri, Gregory W. Wornell |
| 2019 | Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model. Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu |
| 2019 | Learning Nonsymmetric Determinantal Point Processes. Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene |
| 2019 | Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni |
| 2019 | Learning Perceptual Inference by Contrasting. Chi Zhang, Baoxiong Jia, Feng Gao, Yixin Zhu, Hongjing Lu, Song-Chun Zhu |
| 2019 | Learning Positive Functions with Pseudo Mirror Descent. Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He |
| 2019 | Learning Representations by Maximizing Mutual Information Across Views. Philip Bachman, R. Devon Hjelm, William Buchwalter |
| 2019 | Learning Representations for Time Series Clustering. Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell |
| 2019 | Learning Reward Machines for Partially Observable Reinforcement Learning. Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen, Richard Anthony Valenzano, Margarita P. Castro, Sheila A. McIlraith |
| 2019 | Learning Robust Global Representations by Penalizing Local Predictive Power. Haohan Wang, Songwei Ge, Zachary C. Lipton, Eric P. Xing |
| 2019 | Learning Robust Options by Conditional Value at Risk Optimization. Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka |
| 2019 | Learning Sample-Specific Models with Low-Rank Personalized Regression. Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing |
| 2019 | Learning Sparse Distributions using Iterative Hard Thresholding. Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo |
| 2019 | Learning Stable Deep Dynamics Models. J. Zico Kolter, Gaurav Manek |
| 2019 | Learning Temporal Pose Estimation from Sparsely-Labeled Videos. Gedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, Lorenzo Torresani |
| 2019 | Learning Transferable Graph Exploration. Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli |
| 2019 | Learning about an exponential amount of conditional distributions. Mohamed Ishmael Belghazi, Maxime Oquab, David Lopez-Paz |
| 2019 | Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers. Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang |
| 2019 | Learning by Abstraction: The Neural State Machine. Drew A. Hudson, Christopher D. Manning |
| 2019 | Learning dynamic polynomial proofs. Alhussein Fawzi, Mateusz Malinowski, Hamza Fawzi, Omar Fawzi |
| 2019 | Learning elementary structures for 3D shape generation and matching. Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry |
| 2019 | Learning from Bad Data via Generation. Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, Dacheng Tao |
| 2019 | Learning from Label Proportions with Generative Adversarial Networks. Jiabin Liu, Bo Wang, Zhiquan Qi, Yingjie Tian, Yong Shi |
| 2019 | Learning from Trajectories via Subgoal Discovery. Sujoy Paul, Jeroen van Baar, Amit K. Roy-Chowdhury |
| 2019 | Learning from brains how to regularize machines. Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias |
| 2019 | Learning in Generalized Linear Contextual Bandits with Stochastic Delays. Zhengyuan Zhou, Renyuan Xu, Jose H. Blanchet |
| 2019 | Learning low-dimensional state embeddings and metastable clusters from time series data. Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang |
| 2019 | Learning metrics for persistence-based summaries and applications for graph classification. Qi Zhao, Yusu Wang |
| 2019 | Learning nonlinear level sets for dimensionality reduction in function approximation. Guannan Zhang, Jiaxin Zhang, Jacob D. Hinkle |
| 2019 | Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. Valerio Perrone, Huibin Shen |
| 2019 | Learning step sizes for unfolded sparse coding. Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort |
| 2019 | Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder. Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou |
| 2019 | Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity. Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros |
| 2019 | Learning to Correlate in Multi-Player General-Sum Sequential Games. Andrea Celli, Alberto Marchesi, Tommaso Bianchi, Nicola Gatti |
| 2019 | Learning to Infer Implicit Surfaces without 3D Supervision. Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li |
| 2019 | Learning to Learn By Self-Critique. Antreas Antoniou, Amos J. Storkey |
| 2019 | Learning to Optimize in Swarms. Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen |
| 2019 | Learning to Perform Local Rewriting for Combinatorial Optimization. Xinyun Chen, Yuandong Tian |
| 2019 | Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer. Wenzheng Chen, Huan Ling, Jun Gao, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler |
| 2019 | Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis. Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, Hongsheng Li |
| 2019 | Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. C. Daniel Freeman, David Ha, Luke Metz |
| 2019 | Learning to Propagate for Graph Meta-Learning. Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang |
| 2019 | Learning to Screen. Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran |
| 2019 | Learning to Self-Train for Semi-Supervised Few-Shot Classification. Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele |
| 2019 | Learning-Based Low-Rank Approximations. Piotr Indyk, Ali Vakilian, Yang Yuan |
| 2019 | Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations. Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, Daniela Rus |
| 2019 | Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks. Aaron Voelker, Ivana Kajic, Chris Eliasmith |
| 2019 | Levenshtein Transformer. Jiatao Gu, Changhan Wang, Junbo Zhao |
| 2019 | Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification. Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil |
| 2019 | Likelihood Ratios for Out-of-Distribution Detection. Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan |
| 2019 | Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao |
| 2019 | Limitations of Lazy Training of Two-layers Neural Network. Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari |
| 2019 | Limitations of the empirical Fisher approximation for natural gradient descent. Frederik Kunstner, Philipp Hennig, Lukas Balles |
| 2019 | Limiting Extrapolation in Linear Approximate Value Iteration. Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill |
| 2019 | Limits of Private Learning with Access to Public Data. Raef Bassily, Shay Moran, Noga Alon |
| 2019 | Linear Stochastic Bandits Under Safety Constraints. Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis |
| 2019 | List-decodable Linear Regression. Sushrut Karmalkar, Adam R. Klivans, Pravesh Kothari |
| 2019 | LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition. Zuxuan Wu, Caiming Xiong, Yu-Gang Jiang, Larry S. Davis |
| 2019 | Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning. Gregory Farquhar, Shimon Whiteson, Jakob N. Foerster |
| 2019 | Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization. Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck R. Cadambe |
| 2019 | Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond. Lin Chen, Hossein Esfandiari, Gang Fu, Vahab S. Mirrokni |
| 2019 | Localized Structured Prediction. Carlo Ciliberto, Francis R. Bach, Alessandro Rudi |
| 2019 | Locally Private Gaussian Estimation. Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu |
| 2019 | Locally Private Learning without Interaction Requires Separation. Amit Daniely, Vitaly Feldman |
| 2019 | Logarithmic Regret for Online Control. Naman Agarwal, Elad Hazan, Karan Singh |
| 2019 | Lookahead Optimizer: k steps forward, 1 step back. Michael R. Zhang, James Lucas, Jimmy Ba, Geoffrey E. Hinton |
| 2019 | Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees. Alix Lhéritier, Frédéric Cazals |
| 2019 | Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing. Jonas Mueller, Vasilis Syrgkanis, Matt Taddy |
| 2019 | Lower Bounds on Adversarial Robustness from Optimal Transport. Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal |
| 2019 | MAVEN: Multi-Agent Variational Exploration. Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson |
| 2019 | MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine |
| 2019 | MaCow: Masked Convolutional Generative Flow. Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard H. Hovy |
| 2019 | Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis |
| 2019 | Machine Teaching of Active Sequential Learners. Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski |
| 2019 | Making AI Forget You: Data Deletion in Machine Learning. Antonio Ginart, Melody Y. Guan, Gregory Valiant, James Zou |
| 2019 | Making the Cut: A Bandit-based Approach to Tiered Interviewing. Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson |
| 2019 | Manifold denoising by Nonlinear Robust Principal Component Analysis. He Lyu, Ningyu Sha, Shuyang Qin, Ming Yan, Yuying Xie, Rongrong Wang |
| 2019 | Manifold-regression to predict from MEG/EEG brain signals without source modeling. David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann |
| 2019 | Manipulating a Learning Defender and Ways to Counteract. Jiarui Gan, Qingyu Guo, Long Tran-Thanh, Bo An, Michael J. Wooldridge |
| 2019 | Mapping State Space using Landmarks for Universal Goal Reaching. Zhiao Huang, Fangchen Liu, Hao Su |
| 2019 | Margin-Based Generalization Lower Bounds for Boosted Classifiers. Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson |
| 2019 | MarginGAN: Adversarial Training in Semi-Supervised Learning. Jinhao Dong, Tong Lin |
| 2019 | Markov Random Fields for Collaborative Filtering. Harald Steck |
| 2019 | Massively scalable Sinkhorn distances via the Nyström method. Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed |
| 2019 | Max-value Entropy Search for Multi-Objective Bayesian Optimization. Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa |
| 2019 | MaxGap Bandit: Adaptive Algorithms for Approximate Ranking. Sumeet Katariya, Ardhendu Tripathy, Robert D. Nowak |
| 2019 | Maximum Entropy Monte-Carlo Planning. Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller |
| 2019 | Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards. Falcon Z. Dai, Matthew R. Walter |
| 2019 | Maximum Mean Discrepancy Gradient Flow. Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton |
| 2019 | McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds. Rui Ray Zhang, Xingwu Liu, Yuyi Wang, Liwei Wang |
| 2019 | MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis. Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brébisson, Yoshua Bengio, Aaron C. Courville |
| 2019 | Memory Efficient Adaptive Optimization. Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer |
| 2019 | Memory-oriented Decoder for Light Field Salient Object Detection. Miao Zhang, Jingjing Li, Ji Wei, Yongri Piao, Huchuan Lu |
| 2019 | Meta Architecture Search. Albert E. Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai |
| 2019 | Meta Learning with Relational Information for Short Sequences. Yujia Xie, Haoming Jiang, Feng Liu, Tuo Zhao, Hongyuan Zha |
| 2019 | Meta-Curvature. Eunbyung Park, Junier B. Oliva |
| 2019 | Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon |
| 2019 | Meta-Learning Representations for Continual Learning. Khurram Javed, Martha White |
| 2019 | Meta-Learning with Implicit Gradients. Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine |
| 2019 | Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition. Satoshi Tsutsui, Yanwei Fu, David J. Crandall |
| 2019 | Meta-Surrogate Benchmarking for Hyperparameter Optimization. Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González |
| 2019 | Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting. Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng |
| 2019 | MetaInit: Initializing learning by learning to initialize. Yann N. Dauphin, Samuel S. Schoenholz |
| 2019 | MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization. Shangyu Chen, Wenya Wang, Sinno Jialin Pan |
| 2019 | Metalearned Neural Memory. Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler |
| 2019 | Metamers of neural networks reveal divergence from human perceptual systems. Jenelle Feather, Alex Durango, Ray Gonzalez, Josh H. McDermott |
| 2019 | Metric Learning for Adversarial Robustness. Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray |
| 2019 | Minimal Variance Sampling in Stochastic Gradient Boosting. Bulat Ibragimov, Gleb Gusev |
| 2019 | Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases. Xiyang Liu, Sewoong Oh |
| 2019 | Minimizers of the Empirical Risk and Risk Monotonicity. Marco Loog, Tom J. Viering, Alexander Mey |
| 2019 | Minimum Stein Discrepancy Estimators. Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey |
| 2019 | Mining GOLD Samples for Conditional GANs. Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin |
| 2019 | MintNet: Building Invertible Neural Networks with Masked Convolutions. Yang Song, Chenlin Meng, Stefano Ermon |
| 2019 | Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption. Wei Ma, George H. Chen |
| 2019 | MixMatch: A Holistic Approach to Semi-Supervised Learning. David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel |
| 2019 | Mixtape: Breaking the Softmax Bottleneck Efficiently. Zhilin Yang, Thang Luong, Ruslan Salakhutdinov, Quoc V. Le |
| 2019 | Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation. Samuel K. Ainsworth, Matt Barnes, Siddhartha S. Srinivasa |
| 2019 | Model Compression with Adversarial Robustness: A Unified Optimization Framework. Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu |
| 2019 | Model Selection for Contextual Bandits. Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo |
| 2019 | Model Similarity Mitigates Test Set Overuse. Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht |
| 2019 | Modeling Conceptual Understanding in Image Reference Games. Rodolfo Corona, Stephan Alaniz, Zeynep Akata |
| 2019 | Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes. Lingge Li, Dustin S. Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi |
| 2019 | Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations. Kevin Smith, Lingjie Mei, Shunyu Yao, Jiajun Wu, Elizabeth S. Spelke, Josh Tenenbaum, Tomer D. Ullman |
| 2019 | Modeling Tabular data using Conditional GAN. Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni |
| 2019 | Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections. Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik |
| 2019 | Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians. Axel Brando, José A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio |
| 2019 | Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach. Shuyue Hu, Chin-wing Leung, Ho-fung Leung |
| 2019 | Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains. Elliot Meyerson, Risto Miikkulainen |
| 2019 | Momentum-Based Variance Reduction in Non-Convex SGD. Ashok Cutkosky, Francesco Orabona |
| 2019 | MonoForest framework for tree ensemble analysis. Igor Kuralenok, Vasilii Ershov, Igor Labutin |
| 2019 | More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation. Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, David D. Cox |
| 2019 | Multi-Agent Common Knowledge Reinforcement Learning. Christian Schröder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson |
| 2019 | Multi-Criteria Dimensionality Reduction with Applications to Fairness. Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, Santosh S. Vempala |
| 2019 | Multi-Resolution Weak Supervision for Sequential Data. Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Alan Fries, Daniel Y. Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré |
| 2019 | Multi-View Reinforcement Learning. Minne Li, Lisheng Wu, Jun Wang, Haitham Bou-Ammar |
| 2019 | Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. Xuesong Niu, Hu Han, Shiguang Shan, Xilin Chen |
| 2019 | Multi-mapping Image-to-Image Translation via Learning Disentanglement. Xiaoming Yu, Yuanqi Chen, Shan Liu, Thomas H. Li, Ge Li |
| 2019 | Multi-marginal Wasserstein GAN. Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan |
| 2019 | Multi-objective Bayesian optimisation with preferences over objectives. Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh |
| 2019 | Multi-objects Generation with Amortized Structural Regularization. Taufik Xu, Chongxuan Li, Jun Zhu, Bo Zhang |
| 2019 | Multi-relational Poincaré Graph Embeddings. Ivana Balazevic, Carl Allen, Timothy M. Hospedales |
| 2019 | Multi-resolution Multi-task Gaussian Processes. Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark A. Girolami |
| 2019 | Multi-source Domain Adaptation for Semantic Segmentation. Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer |
| 2019 | Multi-task Learning for Aggregated Data using Gaussian Processes. Fariba Yousefi, Michael Thomas Smith, Mauricio A. Álvarez |
| 2019 | Multiagent Evaluation under Incomplete Information. Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos |
| 2019 | Multiclass Learning from Contradictions. Sauptik Dhar, Vladimir Cherkassky, Mohak Shah |
| 2019 | Multiclass Performance Metric Elicitation. Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo |
| 2019 | Multilabel reductions: what is my loss optimising? |
| 2019 | Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation. Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim |
| 2019 | Multiple Futures Prediction. Yichuan Charlie Tang, Ruslan Salakhutdinov |
| 2019 | Multivariate Distributionally Robust Convex Regression under Absolute Error Loss. Jose H. Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou |
| 2019 | Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes. Rui Li |
| 2019 | Multivariate Triangular Quantile Maps for Novelty Detection. Jingjing Wang, Sun Sun, Yaoliang Yu |
| 2019 | Multiview Aggregation for Learning Category-Specific Shape Reconstruction. Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas |
| 2019 | Multiway clustering via tensor block models. Miaoyan Wang, Yuchen Zeng |
| 2019 | Mutually Regressive Point Processes. Ifigeneia Apostolopoulou, Scott W. Linderman, Kyle Miller, Artur Dubrawski |
| 2019 | Möbius Transformation for Fast Inner Product Search on Graph. Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li |
| 2019 | N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules. Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang |
| 2019 | NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue |
| 2019 | NAT: Neural Architecture Transformer for Accurate and Compact Architectures. Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen, Peilin Zhao, Junzhou Huang |
| 2019 | Near Neighbor: Who is the Fairest of Them All? Sariel Har-Peled, Sepideh Mahabadi |
| 2019 | Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes. Junzhe Zhang, Elias Bareinboim |
| 2019 | Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi |
| 2019 | Necessary and Sufficient Geometries for Gradient Methods. Daniel Levy, John C. Duchi |
| 2019 | Network Pruning via Transformable Architecture Search. Xuanyi Dong, Yi Yang |
| 2019 | NeurVPS: Neural Vanishing Point Scanning via Conic Convolution. Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma |
| 2019 | Neural Attribution for Semantic Bug-Localization in Student Programs. Rahul Gupta, Aditya Kanade, Shirish K. Shevade |
| 2019 | Neural Diffusion Distance for Image Segmentation. Jian Sun, Zongben Xu |
| 2019 | Neural Jump Stochastic Differential Equations. Junteng Jia, Austin R. Benson |
| 2019 | Neural Lyapunov Control. Ya-Chien Chang, Nima Roohi, Sicun Gao |
| 2019 | Neural Machine Translation with Soft Prototype. Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, Tie-Yan Liu |
| 2019 | Neural Multisensory Scene Inference. Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Chris Pal, Sungjin Ahn |
| 2019 | Neural Networks with Cheap Differential Operators. Tian Qi Chen, David Duvenaud |
| 2019 | Neural Relational Inference with Fast Modular Meta-learning. Ferran Alet, Erica Weng, Tomás Lozano-Pérez, Leslie Pack Kaelbling |
| 2019 | Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time. Karlis Freivalds, Emils Ozolins, Agris Sostaks |
| 2019 | Neural Similarity Learning. Weiyang Liu, Zhen Liu, James M. Rehg, Le Song |
| 2019 | Neural Spline Flows. Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios |
| 2019 | Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity. Aria Y. Wang, Michael J. Tarr, Leila Wehbe |
| 2019 | Neural Temporal-Difference Learning Converges to Global Optima. Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang |
| 2019 | Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy. Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2019 | Neural networks grown and self-organized by noise. Guruprasad Raghavan, Matt Thomson |
| 2019 | Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. Ruibo Tu, Kun Zhang, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang |
| 2019 | No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. Max Vladymyrov |
| 2019 | No-Press Diplomacy: Modeling Multi-Agent Gameplay. Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron C. Courville |
| 2019 | No-Regret Learning in Unknown Games with Correlated Payoffs. Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause |
| 2019 | Noise-tolerant fair classification. Alexandre Louis Lamy, Ziyuan Zhong |
| 2019 | Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. Max Simchowitz, Kevin Jamieson |
| 2019 | Non-Asymptotic Pure Exploration by Solving Games. Rémy Degenne, Wouter M. Koolen, Pierre Ménard |
| 2019 | Non-Cooperative Inverse Reinforcement Learning. Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Basar |
| 2019 | Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning. Erwan Lecarpentier, Emmanuel Rachelson |
| 2019 | Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. Yi Xu, Rong Jin, Tianbao Yang |
| 2019 | Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics. Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie |
| 2019 | Nonconvex Low-Rank Tensor Completion from Noisy Data. Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen |
| 2019 | Nonlinear scaling of resource allocation in sensory bottlenecks. Laura Rose Edmondson, Alejandro Jiménez-Rodríguez, Hannes P. Saal |
| 2019 | Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric. Nirandika Wanigasekara, Christina Lee Yu |
| 2019 | Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses. Ananya Uppal, Shashank Singh, Barnabás Póczos |
| 2019 | Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes. Siqi Liu, Milos Hauskrecht |
| 2019 | Nonstochastic Multiarmed Bandits with Unrestricted Delays. Tobias Sommer Thune, Nicolò Cesa-Bianchi, Yevgeny Seldin |
| 2019 | Nonzero-sum Adversarial Hypothesis Testing Games. Sarath Yasodharan, Patrick Loiseau |
| 2019 | Normalization Helps Training of Quantized LSTM. Lu Hou, Jinhua Zhu, James T. Kwok, Fei Gao, Tao Qin, Tie-Yan Liu |
| 2019 | Novel positional encodings to enable tree-based transformers. Vighnesh Leonardo Shiv, Chris Quirk |
| 2019 | Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models. Tao Yu, Christopher De Sa |
| 2019 | ODE2VAE: Deep generative second order ODEs with Bayesian neural networks. Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki |
| 2019 | Object landmark discovery through unsupervised adaptation. Enrique Sanchez, Georgios Tzimiropoulos |
| 2019 | ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz |
| 2019 | Oblivious Sampling Algorithms for Private Data Analysis. Sajin Sasy, Olga Ohrimenko |
| 2019 | Off-Policy Evaluation via Off-Policy Classification. Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine |
| 2019 | Offline Contextual Bandits with High Probability Fairness Guarantees. Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas |
| 2019 | Offline Contextual Bayesian Optimization. Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen |
| 2019 | On Adversarial Mixup Resynthesis. Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Chris Pal |
| 2019 | On Differentially Private Graph Sparsification and Applications. Raman Arora, Jalaj Upadhyay |
| 2019 | On Distributed Averaging for Stochastic k-PCA. Aditya Bhaskara, Maheshakya Wijewardena |
| 2019 | On Exact Computation with an Infinitely Wide Neural Net. Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang |
| 2019 | On Fenchel Mini-Max Learning. Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy V. Bobashev, Lawrence Carin |
| 2019 | On Human-Aligned Risk Minimization. Liu Leqi, Adarsh Prasad, Pradeep Ravikumar |
| 2019 | On Lazy Training in Differentiable Programming. Lénaïc Chizat, Edouard Oyallon, Francis R. Bach |
| 2019 | On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective. Lili Su, Pengkun Yang |
| 2019 | On Making Stochastic Classifiers Deterministic. Andrew Cotter, Maya R. Gupta, Harikrishna Narasimhan |
| 2019 | On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak |
| 2019 | On Relating Explanations and Adversarial Examples. Alexey Ignatiev, Nina Narodytska, João Marques-Silva |
| 2019 | On Robustness of Principal Component Regression. Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song |
| 2019 | On Robustness to Adversarial Examples and Polynomial Optimization. Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan |
| 2019 | On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons. Wenbo Ren, Jia Liu, Ness B. Shroff |
| 2019 | On Single Source Robustness in Deep Fusion Models. Taewan Kim, Joydeep Ghosh |
| 2019 | On Testing for Biases in Peer Review. Ivan Stelmakh, Nihar B. Shah, Aarti Singh |
| 2019 | On The Classification-Distortion-Perception Tradeoff. Dong Liu, Haochen Zhang, Zhiwei Xiong |
| 2019 | On Tractable Computation of Expected Predictions. Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck |
| 2019 | On the (In)fidelity and Sensitivity of Explanations. Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar |
| 2019 | On the Accuracy of Influence Functions for Measuring Group Effects. Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, Percy Liang |
| 2019 | On the Calibration of Multiclass Classification with Rejection. Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama |
| 2019 | On the Convergence Rate of Training Recurrent Neural Networks. Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song |
| 2019 | On the Correctness and Sample Complexity of Inverse Reinforcement Learning. Abi Komanduru, Jean Honorio |
| 2019 | On the Curved Geometry of Accelerated Optimization. Aaron Defazio |
| 2019 | On the Downstream Performance of Compressed Word Embeddings. Avner May, Jian Zhang, Tri Dao, Christopher Ré |
| 2019 | On the Expressive Power of Deep Polynomial Neural Networks. Joe Kileel, Matthew Trager, Joan Bruna |
| 2019 | On the Fairness of Disentangled Representations. Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem |
| 2019 | On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle |
| 2019 | On the Hardness of Robust Classification. Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell |
| 2019 | On the Inductive Bias of Neural Tangent Kernels. Alberto Bietti, Julien Mairal |
| 2019 | On the Ineffectiveness of Variance Reduced Optimization for Deep Learning. Aaron Defazio, Léon Bottou |
| 2019 | On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. Baekjin Kim, Ambuj Tewari |
| 2019 | On the Power and Limitations of Random Features for Understanding Neural Networks. Gilad Yehudai, Ohad Shamir |
| 2019 | On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer |
| 2019 | On the Utility of Learning about Humans for Human-AI Coordination. Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan |
| 2019 | On the Value of Target Data in Transfer Learning. Steve Hanneke, Samory Kpotufe |
| 2019 | On the convergence of single-call stochastic extra-gradient methods. Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos |
| 2019 | On the equivalence between graph isomorphism testing and function approximation with GNNs. Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna |
| 2019 | On the number of variables to use in principal component regression. Ji Xu, Daniel J. Hsu |
| 2019 | On two ways to use determinantal point processes for Monte Carlo integration. Guillaume Gautier, Rémi Bardenet, Michal Valko |
| 2019 | One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian |
| 2019 | One-Shot Object Detection with Co-Attention and Co-Excitation. Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu |
| 2019 | Online Continual Learning with Maximal Interfered Retrieval. Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia |
| 2019 | Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback. Mingrui Zhang, Lin Chen, Hamed Hassani, Amin Karbasi |
| 2019 | Online Convex Matrix Factorization with Representative Regions. Jianhao Peng, Olgica Milenkovic, Abhishek Agarwal |
| 2019 | Online EXP3 Learning in Adversarial Bandits with Delayed Feedback. Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose H. Blanchet |
| 2019 | Online Forecasting of Total-Variation-bounded Sequences. Dheeraj Baby, Yu-Xiang Wang |
| 2019 | Online Learning via the Differential Privacy Lens. Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari |
| 2019 | Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms. Vikas K. Garg, Tamar Pichkhadze |
| 2019 | Online Normalization for Training Neural Networks. Vitaliy Chiley, Ilya Sharapov, Atli Kosson, Urs Köster, Ryan Reece, Sofia Samaniego de la Fuente, Vishal Subbiah, Michael James |
| 2019 | Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis. Yingying Li, Xin Chen, Na Li |
| 2019 | Online Prediction of Switching Graph Labelings with Cluster Specialists. Mark Herbster, James Robinson |
| 2019 | Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function. Aviv Rosenberg, Yishay Mansour |
| 2019 | Online sampling from log-concave distributions. Holden Lee, Oren Mangoubi, Nisheeth K. Vishnoi |
| 2019 | Online-Within-Online Meta-Learning. Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil |
| 2019 | Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation. Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep Ravikumar |
| 2019 | Optimal Best Markovian Arm Identification with Fixed Confidence. Vrettos Moulos |
| 2019 | Optimal Decision Tree with Noisy Outcomes. Su Jia, Viswanath Nagarajan, Fatemeh Navidi, R. Ravi |
| 2019 | Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer. Arsenii Vanunts, Alexey Drutsa |
| 2019 | Optimal Sampling and Clustering in the Stochastic Block Model. Se-Young Yun, Alexandre Proutière |
| 2019 | Optimal Sketching for Kronecker Product Regression and Low Rank Approximation. Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff |
| 2019 | Optimal Sparse Decision Trees. Xiyang Hu, Cynthia Rudin, Margo I. Seltzer |
| 2019 | Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation. Zengfeng Huang, Ziyue Huang, Yilei Wang, Ke Yi |
| 2019 | Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. Dominic Richards, Patrick Rebeschini |
| 2019 | Optimal Stochastic and Online Learning with Individual Iterates. Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou |
| 2019 | Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation. Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann |
| 2019 | Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions. Gabriele Farina, Christian Kroer, Tuomas Sandholm |
| 2019 | Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection. Pan Li, I (Eli) Chien, Olgica Milenkovic |
| 2019 | Optimizing Generalized Rate Metrics with Three Players. Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta |
| 2019 | Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback. Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi |
| 2019 | Order Optimal One-Shot Distributed Learning. Arsalan Sharif-Nassab, Saber Salehkaleybar, S. Jamaloddin Golestani |
| 2019 | Ordered Memory. Yikang Shen, Shawn Tan, Seyed Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron C. Courville |
| 2019 | Ouroboros: On Accelerating Training of Transformer-Based Language Models. Qian Yang, Zhouyuan Huo, Wenlin Wang, Heng Huang, Lawrence Carin |
| 2019 | Outlier Detection and Robust PCA Using a Convex Measure of Innovation. Mostafa Rahmani, Ping Li |
| 2019 | Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart |
| 2019 | Outlier-robust estimation of a sparse linear model using \ell_1-penalized Huber's M-estimator. Arnak S. Dalalyan, Philip Thompson |
| 2019 | PAC-Bayes Un-Expected Bernstein Inequality. Zakaria Mhammedi, Peter Grünwald, Benjamin Guedj |
| 2019 | PAC-Bayes under potentially heavy tails. Matthew J. Holland |
| 2019 | PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong |
| 2019 | PHYRE: A New Benchmark for Physical Reasoning. Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross B. Girshick |
| 2019 | PIDForest: Anomaly Detection via Partial Identification. Parikshit Gopalan, Vatsal Sharan, Udi Wieder |
| 2019 | PRNet: Self-Supervised Learning for Partial-to-Partial Registration. Yue Wang, Justin M. Solomon |
| 2019 | Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien |
| 2019 | Paradoxes in Fair Machine Learning. Paul Gölz, Anson Kahng, Ariel D. Procaccia |
| 2019 | Parameter elimination in particle Gibbs sampling. Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten |
| 2019 | Paraphrase Generation with Latent Bag of Words. Yao Fu, Yansong Feng, John P. Cunningham |
| 2019 | Pareto Multi-Task Learning. Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, Sam Kwong |
| 2019 | Park: An Open Platform for Learning-Augmented Computer Systems. Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh |
| 2019 | Partially Encrypted Deep Learning using Functional Encryption. Théo Ryffel, David Pointcheval, Francis R. Bach, Edouard Dufour-Sans, Romain Gay |
| 2019 | Partitioning Structure Learning for Segmented Linear Regression Trees. Xiangyu Zheng, Song Xi Chen |
| 2019 | PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph. Yikang Li, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang |
| 2019 | Perceiving the arrow of time in autoregressive motion. Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann |
| 2019 | Personalizing Many Decisions with High-Dimensional Covariates. Nima Hamidi, Mohsen Bayati, Kapil Gupta |
| 2019 | PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points. Siyuan Huang, Yixin Chen, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu |
| 2019 | PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments. David Novotný, Benjamin Graham, Jeremy Reizenstein |
| 2019 | Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints. David Simchi-Levi, Yunzong Xu |
| 2019 | Piecewise Strong Convexity of Neural Networks. Tristan Milne |
| 2019 | Planning in entropy-regularized Markov decision processes and games. Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko |
| 2019 | Planning with Goal-Conditioned Policies. Soroush Nasiriany, Vitchyr Pong, Steven Lin, Sergey Levine |
| 2019 | Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games. Emmanouil V. Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras |
| 2019 | Point-Voxel CNN for Efficient 3D Deep Learning. Zhijian Liu, Haotian Tang, Yujun Lin, Song Han |
| 2019 | PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation. Can Qin, Haoxuan You, Lichen Wang, C.-C. Jay Kuo, Yun Fu |
| 2019 | Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. Ruqi Zhang, Christopher De Sa |
| 2019 | Poisson-Randomized Gamma Dynamical Systems. Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach |
| 2019 | Policy Continuation with Hindsight Inverse Dynamics. Hao Sun, Zhizhong Li, Xiaotong Liu, Bolei Zhou, Dahua Lin |
| 2019 | Policy Evaluation with Latent Confounders via Optimal Balance. Andrew Bennett, Nathan Kallus |
| 2019 | Policy Learning for Fairness in Ranking. Ashudeep Singh, Thorsten Joachims |
| 2019 | Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games. Kaiqing Zhang, Zhuoran Yang, Tamer Basar |
| 2019 | Policy Poisoning in Batch Reinforcement Learning and Control. Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu |
| 2019 | Polynomial Cost of Adaptation for X-Armed Bandits. Hédi Hadiji |
| 2019 | Positional Normalization. Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge J. Belongie |
| 2019 | Positive-Unlabeled Compression on the Cloud. Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu |
| 2019 | Post training 4-bit quantization of convolutional networks for rapid-deployment. Ron Banner, Yury Nahshan, Daniel Soudry |
| 2019 | Power analysis of knockoff filters for correlated designs. Jingbo Liu, Philippe Rigollet |
| 2019 | PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi |
| 2019 | Powerset Convolutional Neural Networks. Chris Wendler, Markus Püschel, Dan Alistarh |
| 2019 | Practical Deep Learning with Bayesian Principles. Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota |
| 2019 | Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. David Durfee, Ryan M. Rogers |
| 2019 | Practical Two-Step Lookahead Bayesian Optimization. Jian Wu, Peter I. Frazier |
| 2019 | Practical and Consistent Estimation of f-Divergences. Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin |
| 2019 | Precision-Recall Balanced Topic Modelling. Seppo Virtanen, Mark A. Girolami |
| 2019 | Predicting the Politics of an Image Using Webly Supervised Data. Christopher Thomas, Adriana Kovashka |
| 2019 | Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees. Muhammad Osama, Dave Zachariah, Peter Stoica |
| 2019 | Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models. Farnam Mansouri, Yuxin Chen, Ara Vartanian, Xiaojin (Jerry) Zhu, Adish Singla |
| 2019 | Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Jörn-Henrik Jacobsen |
| 2019 | Primal-Dual Block Generalized Frank-Wolfe. Qi Lei, Jiacheng Zhuo, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis |
| 2019 | Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG. Yujia Jin, Aaron Sidford |
| 2019 | Prior-Free Dynamic Auctions with Low Regret Buyers. Yuan Deng, Jon Schneider, Balasubramanian Sivan |
| 2019 | Privacy Amplification by Mixing and Diffusion Mechanisms. Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek |
| 2019 | Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation. Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, Anderson C. A. Nascimento |
| 2019 | Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces. Baoxiang Wang, Nidhi Hegde |
| 2019 | Private Hypothesis Selection. Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu |
| 2019 | Private Learning Implies Online Learning: An Efficient Reduction. Alon Gonen, Elad Hazan, Shay Moran |
| 2019 | Private Stochastic Convex Optimization with Optimal Rates. Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta |
| 2019 | Private Testing of Distributions via Sample Permutations. Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld |
| 2019 | Probabilistic Logic Neural Networks for Reasoning. Meng Qu, Jian Tang |
| 2019 | Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht |
| 2019 | Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration. Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier, Devon R. Graham |
| 2019 | Program Synthesis and Semantic Parsing with Learned Code Idioms. Eui Chul Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Alex Polozov |
| 2019 | Progressive Augmentation of GANs. Dan Zhang, Anna Khoreva |
| 2019 | Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas |
| 2019 | Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters. Alberto Maria Metelli, Amarildo Likmeta, Marcello Restelli |
| 2019 | Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes. Matt Jordan, Justin Lewis, Alexandros G. Dimakis |
| 2019 | Provable Gradient Variance Guarantees for Black-Box Variational Inference. Justin Domke |
| 2019 | Provable Non-linear Inductive Matrix Completion. Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon |
| 2019 | Provably Efficient Q-Learning with Low Switching Cost. Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang |
| 2019 | Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle. Simon S. Du, Yuping Luo, Ruosong Wang, Hanrui Zhang |
| 2019 | Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost. Zhuoran Yang, Yongxin Chen, Mingyi Hong, Zhaoran Wang |
| 2019 | Provably Powerful Graph Networks. Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman |
| 2019 | Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. Hadi Salman, Jerry Li, Ilya P. Razenshteyn, Pengchuan Zhang, Huan Zhang, Sébastien Bubeck, Greg Yang |
| 2019 | Provably robust boosted decision stumps and trees against adversarial attacks. Maksym Andriushchenko, Matthias Hein |
| 2019 | Pseudo-Extended Markov chain Monte Carlo. Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman |
| 2019 | Pure Exploration with Multiple Correct Answers. Rémy Degenne, Wouter M. Koolen |
| 2019 | Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently. Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi, Tiejun Huang, K. Y. Michael Wong, Si Wu |
| 2019 | Putting An End to End-to-End: Gradient-Isolated Learning of Representations. Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling |
| 2019 | PyTorch: An Imperative Style, High-Performance Deep Learning Library. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala |
| 2019 | Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi |
| 2019 | Quadratic Video Interpolation. Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang |
| 2019 | Quality Aware Generative Adversarial Networks. Parimala Kancharla, Sumohana S. Channappayya |
| 2019 | Quantum Embedding of Knowledge for Reasoning. Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima P. Karanam, L. Venkata Subramaniam |
| 2019 | Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. Yihe Dong, Samuel B. Hopkins, Jerry Li |
| 2019 | Quantum Wasserstein Generative Adversarial Networks. Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu |
| 2019 | Quaternion Knowledge Graph Embeddings. Shuai Zhang, Yi Tay, Lina Yao, Qi Liu |
| 2019 | R2D2: Reliable and Repeatable Detector and Descriptor. Jérôme Revaud, César Roberto de Souza, Martin Humenberger, Philippe Weinzaepfel |
| 2019 | REM: From Structural Entropy to Community Structure Deception. Yiwei Liu, Jiamou Liu, Zijian Zhang, Liehuang Zhu, Angsheng Li |
| 2019 | RSN: Randomized Subspace Newton. Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik |
| 2019 | RUBi: Reducing Unimodal Biases for Visual Question Answering. Rémi Cadène, Corentin Dancette, Hédi Ben-Younes, Matthieu Cord, Devi Parikh |
| 2019 | RUDDER: Return Decomposition for Delayed Rewards. Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter |
| 2019 | Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node. Suhas Jayaram Subramanya, Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnaswamy, Rohan Kadekodi |
| 2019 | Random Path Selection for Continual Learning. Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, Ling Shao |
| 2019 | Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves. Stefan Meintrup, Alexander Munteanu, Dennis Rohde |
| 2019 | Random Projections with Asymmetric Quantization. Xiaoyun Li, Ping Li |
| 2019 | Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu |
| 2019 | Random Tessellation Forests. Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd T. Elliott |
| 2019 | Random deep neural networks are biased towards simple functions. Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd |
| 2019 | Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices. Santosh S. Vempala, Andre Wibisono |
| 2019 | Rates of Convergence for Large-scale Nearest Neighbor Classification. Xingye Qiao, Jiexin Duan, Guang Cheng |
| 2019 | Re-examination of the Role of Latent Variables in Sequence Modeling. Guokun Lai, Zihang Dai, Yiming Yang, Shinjae Yoo |
| 2019 | Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling. Ping Li, Xiaoyun Li, Cun-Hui Zhang |
| 2019 | Real-Time Reinforcement Learning. Simon Ramstedt, Chris Pal |
| 2019 | Reconciling meta-learning and continual learning with online mixtures of tasks. Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller |
| 2019 | Reconciling λ-Returns with Experience Replay. Brett Daley, Christopher Amato |
| 2019 | Recovering Bandits. Ciara Pike-Burke, Steffen Grünewälder |
| 2019 | Recurrent Kernel Networks. Dexiong Chen, Laurent Jacob, Julien Mairal |
| 2019 | Recurrent Registration Neural Networks for Deformable Image Registration. Robin Sandkühler, Simon Andermatt, Grzegorz Bauman, Sylvia Nyilas, Christoph Jud, Philippe C. Cattin |
| 2019 | Recurrent Space-time Graph Neural Networks. Andrei Liviu Nicolicioiu, Iulia Duta, Marius Leordeanu |
| 2019 | Reducing Noise in GAN Training with Variance Reduced Extragradient. Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lacoste-Julien |
| 2019 | Reducing the variance in online optimization by transporting past gradients. Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux |
| 2019 | Reflection Separation using a Pair of Unpolarized and Polarized Images. Youwei Lyu, Zhaopeng Cui, Si Li, Marc Pollefeys, Boxin Shi |
| 2019 | Region Mutual Information Loss for Semantic Segmentation. Shuai Zhao, Yang Wang, Zheng Yang, Deng Cai |
| 2019 | Region-specific Diffeomorphic Metric Mapping. Zhengyang Shen, François-Xavier Vialard, Marc Niethammer |
| 2019 | Regression Planning Networks. Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li Fei-Fei |
| 2019 | Regret Bounds for Learning State Representations in Reinforcement Learning. Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard |
| 2019 | Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems. Young Hun Jung, Ambuj Tewari |
| 2019 | Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function. Zihan Zhang, Xiangyang Ji |
| 2019 | Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives. Wang Chi Cheung |
| 2019 | Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma |
| 2019 | Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning. Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang |
| 2019 | Regularized Gradient Boosting. Corinna Cortes, Mehryar Mohri, Dmitry Storcheus |
| 2019 | Regularized Weighted Low Rank Approximation. Frank Ban, David P. Woodruff, Qiuyi (Richard) Zhang |
| 2019 | Regularizing Trajectory Optimization with Denoising Autoencoders. Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala, Antti Rasmus, Harri Valpola |
| 2019 | Reinforcement Learning with Convex Constraints. Sobhan Miryoosefi, Kianté Brantley, Hal Daumé III, Miroslav Dudík, Robert E. Schapire |
| 2019 | Reliable training and estimation of variance networks. Nicki Skafte Detlefsen, Martin Jørgensen, Søren Hauberg |
| 2019 | ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies. Bao Wang, Zuoqiang Shi, Stanley J. Osher |
| 2019 | Residual Flows for Invertible Generative Modeling. Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen |
| 2019 | Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks. Lixin Fan, Kam Woh Ng, Chee Seng Chan |
| 2019 | Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach. Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng |
| 2019 | Rethinking Kernel Methods for Node Representation Learning on Graphs. Yu Tian, Long Zhao, Xi Peng, Dimitris N. Metaxas |
| 2019 | Rethinking the CSC Model for Natural Images. Dror Simon, Michael Elad |
| 2019 | Retrosynthesis Prediction with Conditional Graph Logic Network. Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song |
| 2019 | Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness. Andrey Malinin, Mark J. F. Gales |
| 2019 | Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo |
| 2019 | Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs. Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay |
| 2019 | Riemannian batch normalization for SPD neural networks. Daniel A. Brooks, Olivier Schwander, Frédéric Barbaresco, Jean-Yves Schneider, Matthieu Cord |
| 2019 | Robust Attribution Regularization. Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha |
| 2019 | Robust Bi-Tempered Logistic Loss Based on Bregman Divergences. Ehsan Amid, Manfred K. Warmuth, Rohan Anil, Tomer Koren |
| 2019 | Robust Multi-agent Counterfactual Prediction. Alexander Peysakhovich, Christian Kroer, Adam Lerer |
| 2019 | Robust Principal Component Analysis with Adaptive Neighbors. Rui Zhang, Hanghang Tong |
| 2019 | Robust and Communication-Efficient Collaborative Learning. Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani |
| 2019 | Robust exploration in linear quadratic reinforcement learning. Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson |
| 2019 | Robustness Verification of Tree-based Models. Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane S. Boning, Cho-Jui Hsieh |
| 2019 | Robustness to Adversarial Perturbations in Learning from Incomplete Data. Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato |
| 2019 | Root Mean Square Layer Normalization. Biao Zhang, Rico Sennrich |
| 2019 | SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models. Linfeng Zhang, Zhanhong Tan, Jiebo Song, Jingwei Chen, Chenglong Bao, Kaisheng Ma |
| 2019 | SGD on Neural Networks Learns Functions of Increasing Complexity. Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak |
| 2019 | SHE: A Fast and Accurate Deep Neural Network for Encrypted Data. Qian Lou, Lei Jiang |
| 2019 | SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits. Etienne Boursier, Vianney Perchet |
| 2019 | SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel |
| 2019 | SPoC: Search-based Pseudocode to Code. Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy Liang |
| 2019 | SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points. Zhize Li |
| 2019 | STAR-Caps: Capsule Networks with Straight-Through Attentive Routing. Karim Ahmed, Lorenzo Torresani |
| 2019 | STREETS: A Novel Camera Network Dataset for Traffic Flow. Corey Snyder, Minh Do |
| 2019 | Saccader: Improving Accuracy of Hard Attention Models for Vision. Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le |
| 2019 | Safe Exploration for Interactive Machine Learning. Matteo Turchetta, Felix Berkenkamp, Andreas Krause |
| 2019 | Same-Cluster Querying for Overlapping Clusters. Wasim Huleihel, Arya Mazumdar, Muriel Médard, Soumyabrata Pal |
| 2019 | Sample Adaptive MCMC. Michael Zhu |
| 2019 | Sample Complexity of Learning Mixture of Sparse Linear Regressions. Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal |
| 2019 | Sample Efficient Active Learning of Causal Trees. Kristjan H. Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adserà, Guy Bresler |
| 2019 | Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update. Su Young Lee, Sung-Ik Choi, Sae-Young Chung |
| 2019 | Sampled Softmax with Random Fourier Features. Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar |
| 2019 | Sampling Networks and Aggregate Simulation for Online POMDP Planning. Hao Cui, Roni Khardon |
| 2019 | Sampling Sketches for Concave Sublinear Functions of Frequencies. Edith Cohen, Ofir Geri |
| 2019 | Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. Creighton Heaukulani, Mark van der Wilk |
| 2019 | Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski |
| 2019 | Scalable Deep Generative Relational Model with High-Order Node Dependence. Xuhui Fan, Bin Li, Caoyuan Li, Scott A. Sisson, Ling Chen |
| 2019 | Scalable Global Optimization via Local Bayesian Optimization. David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek |
| 2019 | Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching. Hongteng Xu, Dixin Luo, Lawrence Carin |
| 2019 | Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig |
| 2019 | Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. Dominik Linzner, Michael Schmidt, Heinz Koeppl |
| 2019 | Scalable inference of topic evolution via models for latent geometric structures. Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen |
| 2019 | Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations. Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein |
| 2019 | Screening Sinkhorn Algorithm for Regularized Optimal Transport. Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy |
| 2019 | Search on the Replay Buffer: Bridging Planning and Reinforcement Learning. Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine |
| 2019 | Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks. Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum |
| 2019 | Secretary Ranking with Minimal Inversions. Sepehr Assadi, Eric Balkanski, Renato Paes Leme |
| 2019 | Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network. Jennifer L. Cardona, Michael F. Howland, John O. Dabiri |
| 2019 | Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression. Ruidi Chen, Ioannis Ch. Paschalidis |
| 2019 | Selecting causal brain features with a single conditional independence test per feature. Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing |
| 2019 | Selecting the independent coordinates of manifolds with large aspect ratios. Yu-Chia Chen, Marina Meila |
| 2019 | Selective Sampling-based Scalable Sparse Subspace Clustering. Shin Matsushima, Maria Brbic |
| 2019 | Self-Critical Reasoning for Robust Visual Question Answering. Jialin Wu, Raymond J. Mooney |
| 2019 | Self-Routing Capsule Networks. Taeyoung Hahn, Myeongjang Pyeon, Gunhee Kim |
| 2019 | Self-Supervised Deep Learning on Point Clouds by Reconstructing Space. Jonathan Sauder, Bjarne Sievers |
| 2019 | Self-Supervised Generalisation with Meta Auxiliary Learning. Shikun Liu, Andrew J. Davison, Edward Johns |
| 2019 | Self-attention with Functional Time Representation Learning. Da Xu, Chuanwei Ruan, Evren Körpeoglu, Sushant Kumar, Kannan Achan |
| 2019 | Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game. Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Linxiao Yang, Ngai-Man Cheung |
| 2019 | Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos. Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, Wenwu Zhu |
| 2019 | Semantic-Guided Multi-Attention Localization for Zero-Shot Learning. Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal |
| 2019 | Semi-Implicit Graph Variational Auto-Encoders. Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna R. Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian |
| 2019 | Semi-Parametric Dynamic Contextual Pricing. Virag Shah, Ramesh Johari, Jose H. Blanchet |
| 2019 | Semi-Parametric Efficient Policy Learning with Continuous Actions. Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis |
| 2019 | Semi-flat minima and saddle points by embedding neural networks to overparameterization. Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka |
| 2019 | Semi-supervisedly Co-embedding Attributed Networks. Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao |
| 2019 | Sequence Modeling with Unconstrained Generation Order. Dmitrii Emelianenko, Elena Voita, Pavel Serdyukov |
| 2019 | Sequential Experimental Design for Transductive Linear Bandits. Tanner Fiez, Lalit Jain, Kevin Jamieson, Lillian J. Ratliff |
| 2019 | Sequential Neural Processes. Gautam Singh, Jaesik Yoon, Youngsung Son, Sungjin Ahn |
| 2019 | Shadowing Properties of Optimization Algorithms. Antonio Orvieto, Aurélien Lucchi |
| 2019 | Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices. Don Kurian Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, Prateek Jain |
| 2019 | Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models. Vincent Le Guen, Nicolas Thome |
| 2019 | Shaping Belief States with Generative Environment Models for RL. Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aäron van den Oord |
| 2019 | Sim2real transfer learning for 3D human pose estimation: motion to the rescue. Carl Doersch, Andrew Zisserman |
| 2019 | Single-Model Uncertainties for Deep Learning. Natasa Tagasovska, David Lopez-Paz |
| 2019 | Singleshot : a scalable Tucker tensor decomposition. Abraham Traoré, Maxime Berar, Alain Rakotomamonjy |
| 2019 | Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto |
| 2019 | Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices. Vincent S. Chen, Sen Wu, Alexander J. Ratner, Jen Weng, Christopher Ré |
| 2019 | Sliced Gromov-Wasserstein. Titouan Vayer, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel |
| 2019 | Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity. Chulhee Yun, Suvrit Sra, Ali Jadbabaie |
| 2019 | Smoothing Structured Decomposable Circuits. Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli |
| 2019 | Sobolev Independence Criterion. Youssef Mroueh, Tom Sercu, Mattia Rigotti, Inkit Padhi, Cícero Nogueira dos Santos |
| 2019 | Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian D. Reid, Hamid Rezatofighi, Silvio Savarese |
| 2019 | Solving Interpretable Kernel Dimensionality Reduction. Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer G. Dy |
| 2019 | Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods. Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn |
| 2019 | Solving graph compression via optimal transport. Vikas K. Garg, Tommi S. Jaakkola |
| 2019 | SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough |
| 2019 | Space and Time Efficient Kernel Density Estimation in High Dimensions. Arturs Backurs, Piotr Indyk, Tal Wagner |
| 2019 | Sparse High-Dimensional Isotonic Regression. David Gamarnik, Julia Gaudio |
| 2019 | Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis |
| 2019 | Sparse Variational Inference: Bayesian Coresets from Scratch. Trevor Campbell, Boyan Beronov |
| 2019 | Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization. Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li |
| 2019 | Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs. Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata, Takeshi Kurashima, Maya Okawa, Yasunori Akagi, Hiroyuki Toda |
| 2019 | Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour |
| 2019 | Spectral Modification of Graphs for Improved Spectral Clustering. Ioannis Koutis, Huong Le |
| 2019 | Spherical Text Embedding. Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance M. Kaplan, Jiawei Han |
| 2019 | SpiderBoost and Momentum: Faster Variance Reduction Algorithms. Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh |
| 2019 | Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks. Wenrui Zhang, Peng Li |
| 2019 | Splitting Steepest Descent for Growing Neural Architectures. Lemeng Wu, Dilin Wang, Qiang Liu |
| 2019 | Stability of Graph Scattering Transforms. Fernando Gama, Alejandro Ribeiro, Joan Bruna |
| 2019 | Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine |
| 2019 | Stacked Capsule Autoencoders. Adam R. Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton |
| 2019 | Stagewise Training Accelerates Convergence of Testing Error Over SGD. Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang |
| 2019 | Stand-Alone Self-Attention in Vision Models. Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens |
| 2019 | State Aggregation Learning from Markov Transition Data. Yaqi Duan, Zheng Tracy Ke, Mengdi Wang |
| 2019 | Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection. Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo |
| 2019 | Statistical Model Aggregation via Parameter Matching. Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang |
| 2019 | Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem. Gonzalo Mena, Jonathan Niles-Weed |
| 2019 | Statistical-Computational Tradeoff in Single Index Models. Lingxiao Wang, Zhuoran Yang, Zhaoran Wang |
| 2019 | Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling. Andrey Kolobov, Yuval Peres, Cheng Lu, Eric Horvitz |
| 2019 | Stein Variational Gradient Descent With Matrix-Valued Kernels. Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu |
| 2019 | Stochastic Bandits with Context Distributions. Johannes Kirschner, Andreas Krause |
| 2019 | Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match. Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen |
| 2019 | Stochastic Frank-Wolfe for Composite Convex Minimization. Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher |
| 2019 | Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. Difan Zou, Pan Xu, Quanquan Gu |
| 2019 | Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. Adil Salim, Dmitry Kovalev, Peter Richtárik |
| 2019 | Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. Xuechen Li, Yi Wu, Lester Mackey |
| 2019 | Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L. Sharpnack |
| 2019 | Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization. Adithya M. Devraj, Jianshu Chen |
| 2019 | Strategizing against No-regret Learners. Yuan Deng, Jon Schneider, Balasubramanian Sivan |
| 2019 | Streaming Bayesian Inference for Crowdsourced Classification. Edoardo Manino, Long Tran-Thanh, Nicholas R. Jennings |
| 2019 | Structure Learning with Side Information: Sample Complexity. Saurabh Sihag, Ali Tajer |
| 2019 | Structured Graph Learning Via Laplacian Spectral Constraints. Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar |
| 2019 | Structured Prediction with Projection Oracles. Mathieu Blondel |
| 2019 | Structured Variational Inference in Continuous Cox Process Models. Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps |
| 2019 | Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks. Dina Obeid, Hugo Ramambason, Cengiz Pehlevan |
| 2019 | Submodular Function Minimization with Noisy Evaluation Oracle. Shinji Ito |
| 2019 | Subquadratic High-Dimensional Hierarchical Clustering. Amir Abboud, Vincent Cohen-Addad, Hussein Houdrouge |
| 2019 | Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks. Yiwen Guo, Ziang Yan, Changshui Zhang |
| 2019 | Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections. Boris Muzellec, Marco Cuturi |
| 2019 | Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek |
| 2019 | SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems. Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman |
| 2019 | Superposition of many models into one. Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen |
| 2019 | Superset Technique for Approximate Recovery in One-Bit Compressed Sensing. Larkin Flodin, Venkata Gandikota, Arya Mazumdar |
| 2019 | Surfing: Iterative Optimization Over Incrementally Trained Deep Networks. Ganlin Song, Zhou Fan, John Lafferty |
| 2019 | Surrogate Objectives for Batch Policy Optimization in One-step Decision Making. Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans |
| 2019 | Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks. Hosein Hasani, Mahdieh Soleymani, Hamid Aghajan |
| 2019 | SySCD: A System-Aware Parallel Coordinate Descent Algorithm. Nikolas Ioannou, Celestine Mendler-Dünner, Thomas P. Parnell |
| 2019 | Symmetry-Based Disentangled Representation Learning requires Interaction with Environments. Hugo Caselles-Dupré, Michaël Garcia Ortiz, David Filliat |
| 2019 | Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. Niklas W. A. Gebauer, Michael Gastegger, Kristof Schütt |
| 2019 | TAB-VCR: Tags and Attributes based VCR Baselines. Jingxiang Lin, Unnat Jain, Alexander G. Schwing |
| 2019 | Teaching Multiple Concepts to a Forgetful Learner. Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla |
| 2019 | Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. Sawyer Birnbaum, Volodymyr Kuleshov, S. Zayd Enam, Pang Wei Koh, Stefano Ermon |
| 2019 | Tensor Monte Carlo: Particle Methods for the GPU era. Laurence Aitchison |
| 2019 | Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning. Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen |
| 2019 | The Broad Optimality of Profile Maximum Likelihood. Yi Hao, Alon Orlitsky |
| 2019 | The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data. Amanda Gentzel, Dan Garant, David D. Jensen |
| 2019 | The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers. Alex X. Lu, Amy X. Lu, Wiebke Schormann, Marzyeh Ghassemi, David W. Andrews, Alan M. Moses |
| 2019 | The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies. Ronen Basri, David W. Jacobs, Yoni Kasten, Shira Kritchman |
| 2019 | The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. Nathan Kallus, Angela Zhou |
| 2019 | The Functional Neural Process. Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling |
| 2019 | The Geometry of Deep Networks: Power Diagram Subdivision. Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk |
| 2019 | The Impact of Regularization on High-dimensional Logistic Regression. Fariborz Salehi, Ehsan Abbasi, Babak Hassibi |
| 2019 | The Implicit Bias of AdaGrad on Separable Data. Qian Qian, Xiaoyuan Qian |
| 2019 | The Implicit Metropolis-Hastings Algorithm. Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov |
| 2019 | The Label Complexity of Active Learning from Observational Data. Songbai Yan, Kamalika Chaudhuri, Tara Javidi |
| 2019 | The Landscape of Non-convex Empirical Risk with Degenerate Population Risk. Shuang Li, Gongguo Tang, Michael B. Wakin |
| 2019 | The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks. Ryo Karakida, Shotaro Akaho, Shun-ichi Amari |
| 2019 | The Option Keyboard: Combining Skills in Reinforcement Learning. André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup |
| 2019 | The Parameterized Complexity of Cascading Portfolio Scheduling. Eduard Eiben, Robert Ganian, Iyad Kanj, Stefan Szeider |
| 2019 | The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection. Vladimir V. Kniaz, Vladimir A. Knyaz, Fabio Remondino |
| 2019 | The Randomized Midpoint Method for Log-Concave Sampling. Ruoqi Shen, Yin Tat Lee |
| 2019 | The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli |
| 2019 | The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic. Arash Ardakani, Zhengyun Ji, Amir Ardakani, Warren J. Gross |
| 2019 | The Thermodynamic Variational Objective. Vaden Masrani, Tuan Anh Le, Frank Wood |
| 2019 | The continuous Bernoulli: fixing a pervasive error in variational autoencoders. Gabriel Loaiza-Ganem, John P. Cunningham |
| 2019 | The spiked matrix model with generative priors. Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová |
| 2019 | Theoretical Analysis of Adversarial Learning: A Minimax Approach. Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao |
| 2019 | Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. Igor Colin, Ludovic Dos Santos, Kevin Scaman |
| 2019 | Theoretical evidence for adversarial robustness through randomization. Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif |
| 2019 | Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting. Rajat Sen, Hsiang-Fu Yu, Inderjit S. Dhillon |
| 2019 | Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging. Pooria Joulani, András György, Csaba Szepesvári |
| 2019 | Thinning for Accelerating the Learning of Point Processes. Tianbo Li, Yiping Ke |
| 2019 | Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller. Pratyusha Sharma, Deepak Pathak, Abhinav Gupta |
| 2019 | This Looks Like That: Deep Learning for Interpretable Image Recognition. Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan Su |
| 2019 | Thompson Sampling and Approximate Inference. My Phan, Yasin Abbasi-Yadkori, Justin Domke |
| 2019 | Thompson Sampling for Multinomial Logit Contextual Bandits. Min-hwan Oh, Garud Iyengar |
| 2019 | Thompson Sampling with Information Relaxation Penalties. Seungki Min, Costis Maglaras, Ciamac C. Moallemi |
| 2019 | Thresholding Bandit with Optimal Aggregate Regret. Chao Tao, Saúl A. Blanco, Jian Peng, Yuan Zhou |
| 2019 | Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola |
| 2019 | Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD. Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk |
| 2019 | Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels. Michela Meister, Tamás Sarlós, David P. Woodruff |
| 2019 | Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies. Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor |
| 2019 | Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. Yuan Cao, Quanquan Gu |
| 2019 | Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence. Aditya Golatkar, Alessandro Achille, Stefano Soatto |
| 2019 | Time-series Generative Adversarial Networks. Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar |
| 2019 | Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals. Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans |
| 2019 | Topology-Preserving Deep Image Segmentation. Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen |
| 2019 | Total Least Squares Regression in Input Sparsity Time. Huaian Diao, Zhao Song, David P. Woodruff, Xin Yang |
| 2019 | Toward a Characterization of Loss Functions for Distribution Learning. Nika Haghtalab, Cameron Musco, Bo Waggoner |
| 2019 | Towards Automatic Concept-based Explanations. Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim |
| 2019 | Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks. Yuanzhi Li, Colin Wei, Tengyu Ma |
| 2019 | Towards Hardware-Aware Tractable Learning of Probabilistic Models. Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck |
| 2019 | Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende |
| 2019 | Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. Tengyang Xie, Yifei Ma, Yu-Xiang Wang |
| 2019 | Towards Practical Alternating Least-Squares for CCA. Zhiqiang Xu, Ping Li |
| 2019 | Towards Understanding the Importance of Shortcut Connections in Residual Networks. Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao |
| 2019 | Towards a Zero-One Law for Column Subset Selection. Zhao Song, David P. Woodruff, Peilin Zhong |
| 2019 | Towards closing the gap between the theory and practice of SVRG. Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower |
| 2019 | Towards modular and programmable architecture search. Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon, Darshan Patil, Nghia Le, Daniel Ferreira |
| 2019 | Training Image Estimators without Image Ground Truth. Zhihao Xia, Ayan Chakrabarti |
| 2019 | Training Language GANs from Scratch. Cyprien de Masson d'Autume, Shakir Mohamed, Mihaela Rosca, Jack W. Rae |
| 2019 | Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration. Clarice Poon, Jingwei Liang |
| 2019 | Transductive Zero-Shot Learning with Visual Structure Constraint. Ziyu Wan, Dongdong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao |
| 2019 | Transfer Anomaly Detection by Inferring Latent Domain Representations. Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara |
| 2019 | Transfer Learning via Minimizing the Performance Gap Between Domains. Boyu Wang, Jorge A. Mendez, Mingbo Cai, Eric Eaton |
| 2019 | Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan |
| 2019 | Transfusion: Understanding Transfer Learning for Medical Imaging. Maithra Raghu, Chiyuan Zhang, Jon M. Kleinberg, Samy Bengio |
| 2019 | Tree-Sliced Variants of Wasserstein Distances. Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi |
| 2019 | Triad Constraints for Learning Causal Structure of Latent Variables. Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang |
| 2019 | Trivializations for Gradient-Based Optimization on Manifolds. Mario Lezcano Casado |
| 2019 | Trust Region-Guided Proximal Policy Optimization. Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan |
| 2019 | Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels. Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath |
| 2019 | Twin Auxilary Classifiers GAN. Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich |
| 2019 | Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test. Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong Liu, Yu Li, Ling Shao |
| 2019 | Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples. Tengyu Xu, Shaofeng Zou, Yingbin Liang |
| 2019 | U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging. Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel |
| 2019 | Ultra Fast Medoid Identification via Correlated Sequential Halving. Tavor Z. Baharav, David Tse |
| 2019 | Ultrametric Fitting by Gradient Descent. Giovanni Chierchia, Benjamin Perret |
| 2019 | Uncertainty on Asynchronous Time Event Prediction. Bertrand Charpentier, Marin Bilos, Stephan Günnemann |
| 2019 | Uncertainty-based Continual Learning with Adaptive Regularization. Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon |
| 2019 | Unconstrained Monotonic Neural Networks. Antoine Wehenkel, Gilles Louppe |
| 2019 | Uncoupled Regression from Pairwise Comparison Data. Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama |
| 2019 | Understanding Attention and Generalization in Graph Neural Networks. Boris Knyazev, Graham W. Taylor, Mohamed R. Amer |
| 2019 | Understanding Sparse JL for Feature Hashing. Meena Jagadeesan |
| 2019 | Understanding and Improving Layer Normalization. Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin |
| 2019 | Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. Nima Dehmamy, Albert-László Barabási, Rose Yu |
| 2019 | Understanding the Role of Momentum in Stochastic Gradient Methods. Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao |
| 2019 | UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher |
| 2019 | Unified Language Model Pre-training for Natural Language Understanding and Generation. Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon |
| 2019 | Unified Sample-Optimal Property Estimation in Near-Linear Time. Yi Hao, Alon Orlitsky |
| 2019 | Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control. Armin Lederer, Jonas Umlauft, Sandra Hirche |
| 2019 | Uniform convergence may be unable to explain generalization in deep learning. Vaishnavh Nagarajan, J. Zico Kolter |
| 2019 | Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. Joshua Hanson, Maxim Raginsky |
| 2019 | Universal Boosting Variational Inference. Trevor Campbell, Xinglong Li |
| 2019 | Universal Invariant and Equivariant Graph Neural Networks. Nicolas Keriven, Gabriel Peyré |
| 2019 | Universality and individuality in neural dynamics across large populations of recurrent networks. Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo |
| 2019 | Universality in Learning from Linear Measurements. Ehsan Abbasi, Fariborz Salehi, Babak Hassibi |
| 2019 | Unlabeled Data Improves Adversarial Robustness. Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang |
| 2019 | Unlocking Fairness: a Trade-off Revisited. Michael L. Wick, Swetasudha Panda, Jean-Baptiste Tristan |
| 2019 | Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations. Yifeng Fan, Tingran Gao, Zhizhen Zhao |
| 2019 | Unsupervised Curricula for Visual Meta-Reinforcement Learning. Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn |
| 2019 | Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis. David G. Clark, Jesse Livezey, Kristofer E. Bouchard |
| 2019 | Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction. Alban Laflaquière, Michaël Garcia Ortiz |
| 2019 | Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction. Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim |
| 2019 | Unsupervised Learning of Object Keypoints for Perception and Control. Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih |
| 2019 | Unsupervised Meta-Learning for Few-Shot Image Classification. Siavash Khodadadeh, Ladislau Bölöni, Mubarak Shah |
| 2019 | Unsupervised Object Segmentation by Redrawing. Mickaël Chen, Thierry Artières, Ludovic Denoyer |
| 2019 | Unsupervised Scalable Representation Learning for Multivariate Time Series. Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi |
| 2019 | Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video. Jiawang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian D. Reid |
| 2019 | Unsupervised State Representation Learning in Atari. Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm |
| 2019 | Unsupervised learning of object structure and dynamics from videos. Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin P. Murphy, Honglak Lee |
| 2019 | Untangling in Invariant Speech Recognition. Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz H. Elibol, Hanlin Tang, Josh H. McDermott, SueYeon Chung |
| 2019 | Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input. Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier |
| 2019 | User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning. Dirk van der Hoeven |
| 2019 | Using Embeddings to Correct for Unobserved Confounding in Networks. Victor Veitch, Yixin Wang, David M. Blei |
| 2019 | Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song |
| 2019 | Using Statistics to Automate Stochastic Optimization. Hunter Lang, Lin Xiao, Pengchuan Zhang |
| 2019 | Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning. Harm van Seijen, Mehdi Fatemi, Arash Tavakoli |
| 2019 | VIREL: A Variational Inference Framework for Reinforcement Learning. Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson |
| 2019 | Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm. Amir-massoud Farahmand |
| 2019 | Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning. Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong |
| 2019 | Variance Reduced Policy Evaluation with Smooth Function Approximation. Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang |
| 2019 | Variance Reduction for Matrix Games. Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian |
| 2019 | Variance Reduction in Bipartite Experiments through Correlation Clustering. Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab S. Mirrokni |
| 2019 | Variational Bayes under Model Misspecification. Yixin Wang, David M. Blei |
| 2019 | Variational Bayesian Decision-making for Continuous Utilities. Tomasz Kusmierczyk, Joseph Sakaya, Arto Klami |
| 2019 | Variational Bayesian Optimal Experimental Design. Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman |
| 2019 | Variational Denoising Network: Toward Blind Noise Modeling and Removal. Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, Lei Zhang |
| 2019 | Variational Graph Recurrent Neural Networks. Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna R. Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian |
| 2019 | Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr |
| 2019 | Variational Structured Semantic Inference for Diverse Image Captioning. Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang, Xuri Ge, Yongjian Wu, Feiyue Huang, Yan Wang |
| 2019 | Variational Temporal Abstraction. Taesup Kim, Sungjin Ahn, Yoshua Bengio |
| 2019 | Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma |
| 2019 | ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee |
| 2019 | Visual Concept-Metaconcept Learning. Chi Han, Jiayuan Mao, Chuang Gan, Josh Tenenbaum, Jiajun Wu |
| 2019 | Visualizing and Measuring the Geometry of BERT. Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viégas, Andy Coenen, Adam Pearce, Been Kim |
| 2019 | Visualizing the PHATE of Neural Networks. Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne |
| 2019 | Volumetric Correspondence Networks for Optical Flow. Gengshan Yang, Deva Ramanan |
| 2019 | Wasserstein Dependency Measure for Representation Learning. Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aäron van den Oord, Sergey Levine, Pierre Sermanet |
| 2019 | Wasserstein Weisfeiler-Lehman Graph Kernels. Matteo Togninalli, M. Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten M. Borgwardt |
| 2019 | Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior. Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Yung-Yu Chuang |
| 2019 | Weight Agnostic Neural Networks. Adam Gaier, David Ha |
| 2019 | Weighted Linear Bandits for Non-Stationary Environments. Yoan Russac, Claire Vernade, Olivier Cappé |
| 2019 | What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu, Yuanzhi Li |
| 2019 | What the Vec? Towards Probabilistically Grounded Embeddings. Carl Allen, Ivana Balazevic, Timothy M. Hospedales |
| 2019 | When does label smoothing help? Rafael Müller, Simon Kornblith, Geoffrey E. Hinton |
| 2019 | When to Trust Your Model: Model-Based Policy Optimization. Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine |
| 2019 | When to use parametric models in reinforcement learning? Hado van Hasselt, Matteo Hessel, John Aslanides |
| 2019 | Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse |
| 2019 | Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models. Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Lenka Zdeborová |
| 2019 | Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition. Jinwoo Choi, Chen Gao, Joseph C. E. Messou, Jia-Bin Huang |
| 2019 | Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes. Greg Yang |
| 2019 | Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington |
| 2019 | Worst-Case Regret Bounds for Exploration via Randomized Value Functions. Daniel Russo |
| 2019 | Write, Execute, Assess: Program Synthesis with a REPL. Kevin Ellis, Maxwell I. Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, Armando Solar-Lezama |
| 2019 | XLNet: Generalized Autoregressive Pretraining for Language Understanding. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le |
| 2019 | XNAS: Neural Architecture Search with Expert Advice. Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor |
| 2019 | You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong |
| 2019 | ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization. Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David D. Cox |
| 2019 | Zero-Shot Semantic Segmentation. Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez |
| 2019 | Zero-shot Knowledge Transfer via Adversarial Belief Matching. Paul Micaelli, Amos J. Storkey |
| 2019 | Zero-shot Learning via Simultaneous Generating and Learning. Hyeonwoo Yu, Beomhee Lee |
| 2019 | iSplit LBI: Individualized Partial Ranking with Ties via Split LBI. Qianqian Xu, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan Yao |
| 2019 | k-Means Clustering of Lines for Big Data. Yair Marom, Dan Feldman |
| 2019 | muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking. Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu |
| 2019 | q-means: A quantum algorithm for unsupervised machine learning. Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash |
| 2019 | vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang |