| 2018 | (Probably) Concave Graph Matching. Haggai Maron, Yaron Lipman |
| 2018 | 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen |
| 2018 | 3D-Aware Scene Manipulation via Inverse Graphics. Shunyu Yao, Tzu-Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, Bill Freeman, Josh Tenenbaum |
| 2018 | A Bandit Approach to Sequential Experimental Design with False Discovery Control. Kevin Jamieson, Lalit Jain |
| 2018 | A Bayes-Sard Cubature Method. Toni Karvonen, Chris J. Oates, Simo Särkkä |
| 2018 | A Bayesian Approach to Generative Adversarial Imitation Learning. Wonseok Jeon, Seokin Seo, Kee-Eung Kim |
| 2018 | A Bayesian Nonparametric View on Count-Min Sketch. Diana Cai, Michael Mitzenmacher, Ryan P. Adams |
| 2018 | A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon |
| 2018 | A Bridging Framework for Model Optimization and Deep Propagation. Risheng Liu, Shichao Cheng, Xiaokun Liu, Long Ma, Xin Fan, Zhongxuan Luo |
| 2018 | A Convex Duality Framework for GANs. Farzan Farnia, David Tse |
| 2018 | A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents. Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, Changjie Fan |
| 2018 | A Dual Framework for Low-rank Tensor Completion. Madhav Nimishakavi, Pratik Kumar Jawanpuria, Bamdev Mishra |
| 2018 | A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers. Omer Ben-Porat, Moshe Tennenholtz |
| 2018 | A General Method for Amortizing Variational Filtering. Joseph Marino, Milan Cvitkovic, Yisong Yue |
| 2018 | A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks. Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song |
| 2018 | A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication. Peng Jiang, Gagan Agrawal |
| 2018 | A Lyapunov-based Approach to Safe Reinforcement Learning. Yinlam Chow, Ofir Nachum, Edgar A. Duéñez-Guzmán, Mohammad Ghavamzadeh |
| 2018 | A Mathematical Model For Optimal Decisions In A Representative Democracy. Malik Magdon-Ismail, Lirong Xia |
| 2018 | A Model for Learned Bloom Filters and Optimizing by Sandwiching. Michael Mitzenmacher |
| 2018 | A Neural Compositional Paradigm for Image Captioning. Bo Dai, Sanja Fidler, Dahua Lin |
| 2018 | A Practical Algorithm for Distributed Clustering and Outlier Detection. Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang |
| 2018 | A Probabilistic U-Net for Segmentation of Ambiguous Images. Simon Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger |
| 2018 | A Reduction for Efficient LDA Topic Reconstruction. Matteo Almanza, Flavio Chierichetti, Alessandro Panconesi, Andrea Vattani |
| 2018 | A Retrieve-and-Edit Framework for Predicting Structured Outputs. Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang |
| 2018 | A Simple Cache Model for Image Recognition. A. Emin Orhan |
| 2018 | A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization. Zhize Li, Jian Li |
| 2018 | A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin |
| 2018 | A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu |
| 2018 | A Smoother Way to Train Structured Prediction Models. Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui |
| 2018 | A Spectral View of Adversarially Robust Features. Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant |
| 2018 | A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices. Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek B. Archer, David E. Vaillancourt, Vikas Singh, Baba C. Vemuri |
| 2018 | A Stein variational Newton method. Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl |
| 2018 | A Structured Prediction Approach for Label Ranking. Anna Korba, Alexandre Garcia, Florence d'Alché-Buc |
| 2018 | A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice. Hendrik Fichtenberger, Dennis Rohde |
| 2018 | A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation. Alexander H. Liu, Yen-Cheng Liu, Yu-Ying Yeh, Yu-Chiang Frank Wang |
| 2018 | A Unified Framework for Extensive-Form Game Abstraction with Bounds. Christian Kroer, Tuomas Sandholm |
| 2018 | A Unified View of Piecewise Linear Neural Network Verification. Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, Pawan Kumar Mudigonda |
| 2018 | A convex program for bilinear inversion of sparse vectors. Alireza Aghasi, Ali Ahmed, Paul Hand, Babhru Joshi |
| 2018 | A flexible model for training action localization with varying levels of supervision. Guilhem Chéron, Jean-Baptiste Alayrac, Ivan Laptev, Cordelia Schmid |
| 2018 | A loss framework for calibrated anomaly detection. |
| 2018 | A no-regret generalization of hierarchical softmax to extreme multi-label classification. Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski |
| 2018 | A probabilistic population code based on neural samples. Sabyasachi Shivkumar, Richard D. Lange, Ankani Chattoraj, Ralf M. Haefner |
| 2018 | A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization. Cédric Josz, Yi Ouyang, Richard Y. Zhang, Javad Lavaei, Somayeh Sojoudi |
| 2018 | ATOMO: Communication-efficient Learning via Atomic Sparsification. Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris S. Papailiopoulos, Stephen J. Wright |
| 2018 | A^2-Nets: Double Attention Networks. Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng |
| 2018 | Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich |
| 2018 | Acceleration through Optimistic No-Regret Dynamics. Jun-Kun Wang, Jacob D. Abernethy |
| 2018 | Active Learning for Non-Parametric Regression Using Purely Random Trees. Jack Goetz, Ambuj Tewari, Paul M. Zimmerman |
| 2018 | Active Matting. Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin, Rynson W. H. Lau |
| 2018 | Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. Sriram Srinivasan, Marc Lanctot, Vinícius Flores Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, Michael Bowling |
| 2018 | Adaptation to Easy Data in Prediction with Limited Advice. Tobias Sommer Thune, Yevgeny Seldin |
| 2018 | Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning. Tyler R. Scott, Karl Ridgeway, Michael C. Mozer |
| 2018 | Adaptive Learning with Unknown Information Flows. Yonatan Gur, Ahmadreza Momeni |
| 2018 | Adaptive Methods for Nonconvex Optimization. Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar |
| 2018 | Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. Mingrui Liu, Zhe Li, Xiaoyu Wang, Jinfeng Yi, Tianbao Yang |
| 2018 | Adaptive Online Learning in Dynamic Environments. Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou |
| 2018 | Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems. Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi |
| 2018 | Adaptive Sampling Towards Fast Graph Representation Learning. Wen-bing Huang, Tong Zhang, Yu Rong, Junzhou Huang |
| 2018 | Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf |
| 2018 | Adding One Neuron Can Eliminate All Bad Local Minima. Shiyu Liang, Ruoyu Sun, Jason D. Lee, R. Srikant |
| 2018 | Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada. Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, Roman Garnett |
| 2018 | Adversarial Attacks on Stochastic Bandits. Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin (Jerry) Zhu |
| 2018 | Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein |
| 2018 | Adversarial Multiple Source Domain Adaptation. Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, João Paulo Costeira, Geoffrey J. Gordon |
| 2018 | Adversarial Regularizers in Inverse Problems. Sebastian Lunz, Carola Schönlieb, Ozan Öktem |
| 2018 | Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution. Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody |
| 2018 | Adversarial Scene Editing: Automatic Object Removal from Weak Supervision. Rakshith Shetty, Mario Fritz, Bernt Schiele |
| 2018 | Adversarial Text Generation via Feature-Mover's Distance. Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin |
| 2018 | Adversarial vulnerability for any classifier. Alhussein Fawzi, Hamza Fawzi, Omar Fawzi |
| 2018 | Adversarially Robust Generalization Requires More Data. Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry |
| 2018 | Adversarially Robust Optimization with Gaussian Processes. Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher |
| 2018 | Algebraic tests of general Gaussian latent tree models. Dennis Leung, Mathias Drton |
| 2018 | Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation. Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh |
| 2018 | Algorithmic Linearly Constrained Gaussian Processes. Markus Lange-Hegermann |
| 2018 | Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced. Simon S. Du, Wei Hu, Jason D. Lee |
| 2018 | Algorithms and Theory for Multiple-Source Adaptation. Judy Hoffman, Mehryar Mohri, Ningshan Zhang |
| 2018 | Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs. Han Shao, Xiaotian Yu, Irwin King, Michael R. Lyu |
| 2018 | Alternating optimization of decision trees, with application to learning sparse oblique trees. Miguel Á. Carreira-Perpiñán, Pooya Tavallali |
| 2018 | Amortized Inference Regularization. Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon |
| 2018 | An Efficient Pruning Algorithm for Robust Isotonic Regression. Cong Han Lim |
| 2018 | An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression. Sheng Chen, Arindam Banerjee |
| 2018 | An Information-Theoretic Analysis for Thompson Sampling with Many Actions. Shi Dong, Benjamin Van Roy |
| 2018 | An Off-policy Policy Gradient Theorem Using Emphatic Weightings. Ehsan Imani, Eric Graves, Martha White |
| 2018 | An intriguing failing of convolutional neural networks and the CoordConv solution. Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, Jason Yosinski |
| 2018 | Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. Yair Carmon, John C. Duchi |
| 2018 | Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net. Tom Michoel |
| 2018 | Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog. Sang-Woo Lee, Yu-Jung Heo, Byoung-Tak Zhang |
| 2018 | Approximate Knowledge Compilation by Online Collapsed Importance Sampling. Tal Friedman, Guy Van den Broeck |
| 2018 | Approximating Real-Time Recurrent Learning with Random Kronecker Factors. Asier Mujika, Florian Meier, Angelika Steger |
| 2018 | Approximation algorithms for stochastic clustering. David G. Harris, Shi Li, Aravind Srinivasan, Khoa Trinh, Thomas W. Pensyl |
| 2018 | Are GANs Created Equal? A Large-Scale Study. Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet |
| 2018 | Are ResNets Provably Better than Linear Predictors? Ohad Shamir |
| 2018 | Assessing Generative Models via Precision and Recall. Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly |
| 2018 | Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy P. Lillicrap |
| 2018 | Asymptotic optimality of adaptive importance sampling. François Portier, Bernard Delyon |
| 2018 | Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples. Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang |
| 2018 | Attention in Convolutional LSTM for Gesture Recognition. Liang Zhang, Guangming Zhu, Lin Mei, Peiyi Shen, Syed Afaq Ali Shah, Mohammed Bennamoun |
| 2018 | Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. Matthew D. Hoffman |
| 2018 | Automatic Program Synthesis of Long Programs with a Learned Garbage Collector. Amit Zohar, Lior Wolf |
| 2018 | Automatic differentiation in ML: Where we are and where we should be going. Bart van Merrienboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin |
| 2018 | Automating Bayesian optimization with Bayesian optimization. Gustavo Malkomes, Roman Garnett |
| 2018 | BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training. Songtao Wang, Dan Li, Yang Cheng, Jinkun Geng, Yanshu Wang, Shuai Wang, Shu-Tao Xia, Jianping Wu |
| 2018 | BRITS: Bidirectional Recurrent Imputation for Time Series. Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, Yitan Li |
| 2018 | BRUNO: A Deep Recurrent Model for Exchangeable Data. Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre |
| 2018 | Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming. Fei Wang, James M. Decker, Xilun Wu, Grégory M. Essertel, Tiark Rompf |
| 2018 | Balanced Policy Evaluation and Learning. Nathan Kallus |
| 2018 | Banach Wasserstein GAN. Jonas Adler, Sebastian Lunz |
| 2018 | Bandit Learning in Concave N-Person Games. Mario Bravo, David S. Leslie, Panayotis Mertikopoulos |
| 2018 | Bandit Learning with Implicit Feedback. Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun |
| 2018 | Bandit Learning with Positive Externalities. Virag Shah, Jose H. Blanchet, Ramesh Johari |
| 2018 | Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks. Hyeonseob Nam, Hyo-Eun Kim |
| 2018 | Bayesian Adversarial Learning. Nanyang Ye, Zhanxing Zhu |
| 2018 | Bayesian Alignments of Warped Multi-Output Gaussian Processes. Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek |
| 2018 | Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments. Mahdi Imani, Seyede Fatemeh Ghoreishi, Ulisses M. Braga-Neto |
| 2018 | Bayesian Distributed Stochastic Gradient Descent. Michael Teng, Frank Wood |
| 2018 | Bayesian Inference of Temporal Task Specifications from Demonstrations. Ankit Shah, Pritish Kamath, Julie A. Shah, Shen Li |
| 2018 | Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors. Fei Jiang, Guosheng Yin, Francesca Dominici |
| 2018 | Bayesian Model-Agnostic Meta-Learning. Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn |
| 2018 | Bayesian Nonparametric Spectral Estimation. Felipe A. Tobar |
| 2018 | Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC. Tolga Birdal, Umut Simsekli, Mustafa Onur Eken, Slobodan Ilic |
| 2018 | Bayesian Semi-supervised Learning with Graph Gaussian Processes. Yin Cheng Ng, Nicolò Colombo, Ricardo Silva |
| 2018 | Bayesian Structure Learning by Recursive Bootstrap. Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik |
| 2018 | Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian |
| 2018 | Beauty-in-averageness and its contextual modulations: A Bayesian statistical account. Chaitanya Ryali, Angela J. Yu |
| 2018 | Benefits of over-parameterization with EM. Ji Xu, Daniel J. Hsu, Arian Maleki |
| 2018 | Beyond Grids: Learning Graph Representations for Visual Recognition. Yin Li, Abhinav Gupta |
| 2018 | Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. Holden Lee, Andrej Risteski, Rong Ge |
| 2018 | Bias and Generalization in Deep Generative Models: An Empirical Study. Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah D. Goodman, Stefano Ermon |
| 2018 | Bilevel Distance Metric Learning for Robust Image Recognition. Jie Xu, Lei Luo, Cheng Deng, Heng Huang |
| 2018 | Bilevel learning of the Group Lasso structure. Jordan Frécon, Saverio Salzo, Massimiliano Pontil |
| 2018 | Bilinear Attention Networks. Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang |
| 2018 | BinGAN: Learning Compact Binary Descriptors with a Regularized GAN. Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly, Tomasz Trzcinski |
| 2018 | Binary Classification from Positive-Confidence Data. Takashi Ishida, Gang Niu, Masashi Sugiyama |
| 2018 | Binary Rating Estimation with Graph Side Information. Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh |
| 2018 | Bipartite Stochastic Block Models with Tiny Clusters. Stefan Neumann |
| 2018 | Blind Deconvolutional Phase Retrieval via Convex Programming. Ali Ahmed, Alireza Aghasi, Paul Hand |
| 2018 | Blockwise Parallel Decoding for Deep Autoregressive Models. Mitchell Stern, Noam Shazeer, Jakob Uszkoreit |
| 2018 | Boolean Decision Rules via Column Generation. Sanjeeb Dash, Oktay Günlük, Dennis Wei |
| 2018 | Boosted Sparse and Low-Rank Tensor Regression. Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang |
| 2018 | Boosting Black Box Variational Inference. Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch |
| 2018 | Bounded-Loss Private Prediction Markets. Rafael M. Frongillo, Bo Waggoner |
| 2018 | BourGAN: Generative Networks with Metric Embeddings. Chang Xiao, Peilin Zhong, Changxi Zheng |
| 2018 | Breaking the Activation Function Bottleneck through Adaptive Parameterization. Sebastian Flennerhag, Hujun Yin, John A. Keane, Mark J. Elliot |
| 2018 | Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou |
| 2018 | Breaking the Span Assumption Yields Fast Finite-Sum Minimization. Robert Hannah, Yanli Liu, Daniel O'Connor, Wotao Yin |
| 2018 | But How Does It Work in Theory? Linear SVM with Random Features. Yitong Sun, Anna C. Gilbert, Ambuj Tewari |
| 2018 | Byzantine Stochastic Gradient Descent. Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li |
| 2018 | COLA: Decentralized Linear Learning. Lie He, An Bian, Martin Jaggi |
| 2018 | Can We Gain More from Orthogonality Regularizations in Training Deep Networks? Nitin Bansal, Xiaohan Chen, Zhangyang Wang |
| 2018 | CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces. Liheng Zhang, Marzieh Edraki, Guo-Jun Qi |
| 2018 | CatBoost: unbiased boosting with categorical features. Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin |
| 2018 | Causal Discovery from Discrete Data using Hidden Compact Representation. Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao |
| 2018 | Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models. Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Lai-Wan Chan, Yanhui Geng |
| 2018 | Causal Inference via Kernel Deviance Measures. Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh |
| 2018 | Causal Inference with Noisy and Missing Covariates via Matrix Factorization. Nathan Kallus, Xiaojie Mao, Madeleine Udell |
| 2018 | Chain of Reasoning for Visual Question Answering. Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong |
| 2018 | Chaining Mutual Information and Tightening Generalization Bounds. Amir-Reza Asadi, Emmanuel Abbe, Sergio Verdú |
| 2018 | ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. Hongyang Gao, Zhengyang Wang, Shuiwang Ji |
| 2018 | Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network. Risi Kondor, Zhen Lin, Shubhendu Trivedi |
| 2018 | Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. Dominik Linzner, Heinz Koeppl |
| 2018 | Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms. Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn |
| 2018 | Co-regularized Alignment for Unsupervised Domain Adaptation. Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, Bill Freeman, Gregory W. Wornell |
| 2018 | Co-teaching: Robust training of deep neural networks with extremely noisy labels. Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama |
| 2018 | Collaborative Learning for Deep Neural Networks. Guocong Song, Wei Chai |
| 2018 | Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. Zhuwen Li, Qifeng Chen, Vladlen Koltun |
| 2018 | Communication Compression for Decentralized Training. Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu |
| 2018 | Communication Efficient Parallel Algorithms for Optimization on Manifolds. Bayan Saparbayeva, Michael Minyi Zhang, Lizhen Lin |
| 2018 | Community Exploration: From Offline Optimization to Online Learning. Xiaowei Chen, Weiran Huang, Wei Chen, John C. S. Lui |
| 2018 | Compact Generalized Non-local Network. Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu |
| 2018 | Compact Representation of Uncertainty in Clustering. Craig S. Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum |
| 2018 | Completing State Representations using Spectral Learning. Nan Jiang, Alex Kulesza, Satinder Singh |
| 2018 | Complex Gated Recurrent Neural Networks. Moritz Wolter, Angela Yao |
| 2018 | Computationally and statistically efficient learning of causal Bayes nets using path queries. Kevin Bello, Jean Honorio |
| 2018 | Computing Higher Order Derivatives of Matrix and Tensor Expressions. Sören Laue, Matthias Mitterreiter, Joachim Giesen |
| 2018 | Computing Kantorovich-Wasserstein Distances on d-dimensional histograms using (d+1)-partite graphs. Gennaro Auricchio, Federico Bassetti, Stefano Gualandi, Marco Veneroni |
| 2018 | Conditional Adversarial Domain Adaptation. Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan |
| 2018 | Confounding-Robust Policy Improvement. Nathan Kallus, Angela Zhou |
| 2018 | Connecting Optimization and Regularization Paths. Arun Sai Suggala, Adarsh Prasad, Pradeep Ravikumar |
| 2018 | Connectionist Temporal Classification with Maximum Entropy Regularization. Hu Liu, Sheng Jin, Changshui Zhang |
| 2018 | Constant Regret, Generalized Mixability, and Mirror Descent. Zakaria Mhammedi, Robert C. Williamson |
| 2018 | Constrained Cross-Entropy Method for Safe Reinforcement Learning. Min Wen, Ufuk Topcu |
| 2018 | Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders. Tengfei Ma, Jie Chen, Cao Xiao |
| 2018 | Constrained Graph Variational Autoencoders for Molecule Design. Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt |
| 2018 | Constructing Deep Neural Networks by Bayesian Network Structure Learning. Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik |
| 2018 | Constructing Fast Network through Deconstruction of Convolution. Yunho Jeon, Junmo Kim |
| 2018 | Constructing Unrestricted Adversarial Examples with Generative Models. Yang Song, Rui Shu, Nate Kushman, Stefano Ermon |
| 2018 | Contamination Attacks and Mitigation in Multi-Party Machine Learning. Jamie Hayes, Olga Ohrimenko |
| 2018 | Content preserving text generation with attribute controls. Lajanugen Logeswaran, Honglak Lee, Samy Bengio |
| 2018 | Context-aware Synthesis and Placement of Object Instances. Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz |
| 2018 | Context-dependent upper-confidence bounds for directed exploration. Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White |
| 2018 | Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward. Lixing Chen, Jie Xu, Zhuo Lu |
| 2018 | Contextual Pricing for Lipschitz Buyers. Jieming Mao, Renato Paes Leme, Jon Schneider |
| 2018 | Contextual Stochastic Block Models. Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel |
| 2018 | Contextual bandits with surrogate losses: Margin bounds and efficient algorithms. Dylan J. Foster, Akshay Krishnamurthy |
| 2018 | Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama |
| 2018 | Contour location via entropy reduction leveraging multiple information sources. Alexandre Noll Marques, Rémi Lam, Karen Willcox |
| 2018 | Contrastive Learning from Pairwise Measurements. Yi Chen, Zhuoran Yang, Yuchen Xie, Zhaoran Wang |
| 2018 | Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. Yi Zhou, Zhe Wang, Yingbin Liang |
| 2018 | Convex Elicitation of Continuous Properties. Jessica Finocchiaro, Rafael M. Frongillo |
| 2018 | Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation. Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu |
| 2018 | Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization. Bruno Korbar, Du Tran, Lorenzo Torresani |
| 2018 | Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification. Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru |
| 2018 | Coordinate Descent with Bandit Sampling. Farnood Salehi, Patrick Thiran, L. Elisa Celis |
| 2018 | Coupled Variational Bayes via Optimization Embedding. Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song |
| 2018 | Credit Assignment For Collective Multiagent RL With Global Rewards. Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau |
| 2018 | Critical initialisation for deep signal propagation in noisy rectifier neural networks. Arnu Pretorius, Elan Van Biljon, Steve Kroon, Herman Kamper |
| 2018 | DAGs with NO TEARS: Continuous Optimization for Structure Learning. Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing |
| 2018 | DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors. Arash Vahdat, Evgeny Andriyash, William G. Macready |
| 2018 | Data Amplification: A Unified and Competitive Approach to Property Estimation. Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu |
| 2018 | Data center cooling using model-predictive control. Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, M. K. Ryu, Greg Imwalle |
| 2018 | Data-Driven Clustering via Parameterized Lloyd's Families. Maria-Florina Balcan, Travis Dick, Colin White |
| 2018 | Data-Efficient Hierarchical Reinforcement Learning. Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine |
| 2018 | Data-dependent PAC-Bayes priors via differential privacy. Gintare Karolina Dziugaite, Daniel M. Roy |
| 2018 | Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters. Pavel E. Dvurechenskii, Darina Dvinskikh, Alexander V. Gasnikov, César A. Uribe, Angelia Nedich |
| 2018 | Deep Anomaly Detection Using Geometric Transformations. Izhak Golan, Ran El-Yaniv |
| 2018 | Deep Attentive Tracking via Reciprocative Learning. Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang |
| 2018 | Deep Defense: Training DNNs with Improved Adversarial Robustness. Ziang Yan, Yiwen Guo, Changshui Zhang |
| 2018 | Deep Dynamical Modeling and Control of Unsteady Fluid Flows. Jeremy Morton, Antony Jameson, Mykel J. Kochenderfer, Freddie D. Witherden |
| 2018 | Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions. Minhyuk Sung, Hao Su, Ronald Yu, Leonidas J. Guibas |
| 2018 | Deep Generative Markov State Models. Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé |
| 2018 | Deep Generative Models for Distribution-Preserving Lossy Compression. Michael Tschannen, Eirikur Agustsson, Mario Lucic |
| 2018 | Deep Generative Models with Learnable Knowledge Constraints. Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Lianhui Qin, Xiaodan Liang, Haoye Dong, Eric P. Xing |
| 2018 | Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. Priyank Jaini, Pascal Poupart, Yaoliang Yu |
| 2018 | Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images. Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu |
| 2018 | Deep Neural Nets with Interpolating Function as Output Activation. Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi, Stanley J. Osher |
| 2018 | Deep Neural Networks with Box Convolutions. Egor Burkov, Victor S. Lempitsky |
| 2018 | Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation. Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wangmeng Zuo, Wei Liu, Ming-Hsuan Yang |
| 2018 | Deep Poisson gamma dynamical systems. Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou |
| 2018 | Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition. Kuan Han, Haiguang Wen, Yizhen Zhang, Di Fu, Eugenio Culurciello, Zhongming Liu |
| 2018 | Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine |
| 2018 | Deep Reinforcement Learning of Marked Temporal Point Processes. Utkarsh Upadhyay, Abir De, Manuel Gomez Rodriguez |
| 2018 | Deep State Space Models for Time Series Forecasting. Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski |
| 2018 | Deep State Space Models for Unconditional Word Generation. Florian Schmidt, Thomas Hofmann |
| 2018 | Deep Structured Prediction with Nonlinear Output Transformations. Colin Graber, Ofer Meshi, Alexander G. Schwing |
| 2018 | Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres. Oisín Moran, Piergiorgio Caramazza, Daniele Faccio, Roderick Murray-Smith |
| 2018 | DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning. Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao, Bo Zhang |
| 2018 | DeepPINK: reproducible feature selection in deep neural networks. Yang Young Lu, Yingying Fan, Jinchi Lv, William Stafford Noble |
| 2018 | DeepProbLog: Neural Probabilistic Logic Programming. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt |
| 2018 | Deepcode: Feedback Codes via Deep Learning. Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath |
| 2018 | Delta-encoder: an effective sample synthesis method for few-shot object recognition. Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein |
| 2018 | Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation. Chaitanya Ryali, Gautam Reddy, Angela J. Yu |
| 2018 | Dendritic cortical microcircuits approximate the backpropagation algorithm. João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn |
| 2018 | Densely Connected Attention Propagation for Reading Comprehension. Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su |
| 2018 | Depth-Limited Solving for Imperfect-Information Games. Noam Brown, Tuomas Sandholm, Brandon Amos |
| 2018 | Derivative Estimation in Random Design. Yu Liu, Kris De Brabanter |
| 2018 | Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution. Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang |
| 2018 | Dialog-based Interactive Image Retrieval. Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogério Schmidt Feris |
| 2018 | Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base. Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin |
| 2018 | DifNet: Semantic Segmentation by Diffusion Networks. Peng Jiang, Fanglin Gu, Yunhai Wang, Changhe Tu, Baoquan Chen |
| 2018 | Differentiable MPC for End-to-end Planning and Control. Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, J. Zico Kolter |
| 2018 | Differential Privacy for Growing Databases. Rachel Cummings, Sara Krehbiel, Kevin A. Lai, Uthaipon Tao Tantipongpipat |
| 2018 | Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto |
| 2018 | Differentially Private Bayesian Inference for Exponential Families. Garrett Bernstein, Daniel Sheldon |
| 2018 | Differentially Private Change-Point Detection. Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang |
| 2018 | Differentially Private Contextual Linear Bandits. Roshan Shariff, Or Sheffet |
| 2018 | Differentially Private Robust Low-Rank Approximation. Raman Arora, Vladimir Braverman, Jalaj Upadhyay |
| 2018 | Differentially Private Testing of Identity and Closeness of Discrete Distributions. Jayadev Acharya, Ziteng Sun, Huanyu Zhang |
| 2018 | Differentially Private Uniformly Most Powerful Tests for Binomial Data. Jordan Awan, Aleksandra B. Slavkovic |
| 2018 | Differentially Private k-Means with Constant Multiplicative Error. Uri Stemmer, Haim Kaplan |
| 2018 | Diffusion Maps for Textual Network Embedding. Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin |
| 2018 | Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao |
| 2018 | Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds. Kry Yik Chau Lui, Gavin Weiguang Ding, Ruitong Huang, Robert J. McCann |
| 2018 | Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo. Oren Mangoubi, Nisheeth K. Vishnoi |
| 2018 | Diminishing Returns Shape Constraints for Interpretability and Regularization. Maya R. Gupta, Dara Bahri, Andrew Cotter, Kevin Robert Canini |
| 2018 | Direct Estimation of Differences in Causal Graphs. Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler |
| 2018 | Direct Runge-Kutta Discretization Achieves Acceleration. Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie |
| 2018 | Dirichlet belief networks for topic structure learning. He Zhao, Lan Du, Wray L. Buntine, Mingyuan Zhou |
| 2018 | Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone |
| 2018 | Disconnected Manifold Learning for Generative Adversarial Networks. Mahyar Khayatkhoei, Maneesh Singh, Ahmed Elgammal |
| 2018 | Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning. Supasorn Suwajanakorn, Noah Snavely, Jonathan Tompson, Mohammad Norouzi |
| 2018 | Discretely Relaxing Continuous Variables for tractable Variational Inference. Trefor W. Evans, Prasanth B. Nair |
| 2018 | Discrimination-aware Channel Pruning for Deep Neural Networks. Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, Jin-Hui Zhu |
| 2018 | Distilled Wasserstein Learning for Word Embedding and Topic Modeling. Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin |
| 2018 | Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu |
| 2018 | Distributed Multi-Player Bandits - a Game of Thrones Approach. Ilai Bistritz, Amir Leshem |
| 2018 | Distributed Multitask Reinforcement Learning with Quadratic Convergence. Rasul Tutunov, Dongho Kim, Haitham Bou-Ammar |
| 2018 | Distributed Stochastic Optimization via Adaptive SGD. Ashok Cutkosky, Róbert Busa-Fekete |
| 2018 | Distributed Weight Consolidation: A Brain Segmentation Case Study. Patrick McClure, Charles Y. Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satrajit S. Ghosh, Dylan Nielson, Peter A. Bandettini, Francisco Pereira |
| 2018 | Distributed k-Clustering for Data with Heavy Noise. Shi Li, Xiangyu Guo |
| 2018 | Distributionally Robust Graphical Models. Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart |
| 2018 | Diverse Ensemble Evolution: Curriculum Data-Model Marriage. Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes |
| 2018 | Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee |
| 2018 | Do Less, Get More: Streaming Submodular Maximization with Subsampling. Moran Feldman, Amin Karbasi, Ehsan Kazemi |
| 2018 | Does mitigating ML's impact disparity require treatment disparity? Zachary C. Lipton, Julian J. McAuley, Alexandra Chouldechova |
| 2018 | Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij |
| 2018 | Domain-Invariant Projection Learning for Zero-Shot Recognition. An Zhao, Mingyu Ding, Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen |
| 2018 | Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \beta-Divergences. Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas |
| 2018 | DropBlock: A regularization method for convolutional networks. Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le |
| 2018 | DropMax: Adaptive Variational Softmax. Haebeom Lee, Juho Lee, Saehoon Kim, Eunho Yang, Sung Ju Hwang |
| 2018 | Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization. Zhihui Zhu, Xiao Li, Kai Liu, Qiuwei Li |
| 2018 | Dual Policy Iteration. Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell |
| 2018 | Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms. Zhihui Zhu, Yifan Wang, Daniel P. Robinson, Daniel Q. Naiman, René Vidal, Manolis C. Tsakiris |
| 2018 | Dual Swap Disentangling. Zunlei Feng, Xinchao Wang, Chenglong Ke, Anxiang Zeng, Dacheng Tao, Mingli Song |
| 2018 | Dynamic Network Model from Partial Observations. Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec |
| 2018 | Early Stopping for Nonparametric Testing. Meimei Liu, Guang Cheng |
| 2018 | Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization. Francis R. Bach |
| 2018 | Efficient Anomaly Detection via Matrix Sketching. Vatsal Sharan, Parikshit Gopalan, Udi Wieder |
| 2018 | Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms. Kishan Wimalawarne, Hiroshi Mamitsuka |
| 2018 | Efficient Formal Safety Analysis of Neural Networks. Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana |
| 2018 | Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses. Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang |
| 2018 | Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features. Mojmir Mutny, Andreas Krause |
| 2018 | Efficient Loss-Based Decoding on Graphs for Extreme Classification. Itay Evron, Edward Moroshko, Koby Crammer |
| 2018 | Efficient Neural Network Robustness Certification with General Activation Functions. Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel |
| 2018 | Efficient Online Portfolio with Logarithmic Regret. Haipeng Luo, Chen-Yu Wei, Kai Zheng |
| 2018 | Efficient Projection onto the Perfect Phylogeny Model. Bei Jia, Surjyendu Ray, Sam Safavi, José Bento |
| 2018 | Efficient Stochastic Gradient Hard Thresholding. Pan Zhou, Xiaotong Yuan, Jiashi Feng |
| 2018 | Efficient inference for time-varying behavior during learning. Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos D. Brody, Jonathan W. Pillow |
| 2018 | Efficient nonmyopic batch active search. Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley, Roman Garnett |
| 2018 | Efficient online algorithms for fast-rate regret bounds under sparsity. Pierre Gaillard, Olivier Wintenberger |
| 2018 | Embedding Logical Queries on Knowledge Graphs. William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec |
| 2018 | Empirical Risk Minimization Under Fairness Constraints. Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil |
| 2018 | Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. Di Wang, Marco Gaboardi, Jinhui Xu |
| 2018 | End-to-End Differentiable Physics for Learning and Control. Filipe de Avila Belbute-Peres, Kevin A. Smith, Kelsey R. Allen, Josh Tenenbaum, J. Zico Kolter |
| 2018 | End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems. Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E |
| 2018 | Enhancing the Accuracy and Fairness of Human Decision Making. Isabel Valera, Adish Singla, Manuel Gomez Rodriguez |
| 2018 | Entropy Rate Estimation for Markov Chains with Large State Space. Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu |
| 2018 | Entropy and mutual information in models of deep neural networks. Marylou Gabrié, Andre Manoel, Clément Luneau, Jean Barbier, Nicolas Macris, Florent Krzakala, Lenka Zdeborová |
| 2018 | Equality of Opportunity in Classification: A Causal Approach. Junzhe Zhang, Elias Bareinboim |
| 2018 | Escaping Saddle Points in Constrained Optimization. Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie |
| 2018 | Estimating Learnability in the Sublinear Data Regime. Weihao Kong, Gregory Valiant |
| 2018 | Estimators for Multivariate Information Measures in General Probability Spaces. Arman Rahimzamani, Himanshu Asnani, Pramod Viswanath, Sreeram Kannan |
| 2018 | Evidential Deep Learning to Quantify Classification Uncertainty. Murat Sensoy, Lance M. Kaplan, Melih Kandemir |
| 2018 | Evolution-Guided Policy Gradient in Reinforcement Learning. Shauharda Khadka, Kagan Tumer |
| 2018 | Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks. Xiaodong Cui, Wei Zhang, Zoltán Tüske, Michael Picheny |
| 2018 | Evolved Policy Gradients. Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel |
| 2018 | Ex ante coordination and collusion in zero-sum multi-player extensive-form games. Gabriele Farina, Andrea Celli, Nicola Gatti, Tuomas Sandholm |
| 2018 | Exact natural gradient in deep linear networks and its application to the nonlinear case. Alberto Bernacchia, Máté Lengyel, Guillaume Hennequin |
| 2018 | Expanding Holographic Embeddings for Knowledge Completion. Yexiang Xue, Yang Yuan, Zhitian Xu, Ashish Sabharwal |
| 2018 | Experimental Design for Cost-Aware Learning of Causal Graphs. Erik M. Lindgren, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath |
| 2018 | Explaining Deep Learning Models - A Bayesian Non-parametric Approach. Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin |
| 2018 | Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Pai-Shun Ting, Karthikeyan Shanmugam, Payel Das |
| 2018 | Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression. Neha Gupta, Aaron Sidford |
| 2018 | Exploration in Structured Reinforcement Learning. Jungseul Ok, Alexandre Proutière, Damianos Tranos |
| 2018 | Exponentially Weighted Imitation Learning for Batched Historical Data. Qing Wang, Jiechao Xiong, Lei Han, Peng Sun, Han Liu, Tong Zhang |
| 2018 | Exponentiated Strongly Rayleigh Distributions. Zelda E. Mariet, Suvrit Sra, Stefanie Jegelka |
| 2018 | Extracting Relationships by Multi-Domain Matching. Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson |
| 2018 | FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification. Yixiao Ge, Zhuowan Li, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, Hongsheng Li |
| 2018 | FRAGE: Frequency-Agnostic Word Representation. Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan Liu |
| 2018 | Factored Bandits. Julian Zimmert, Yevgeny Seldin |
| 2018 | Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making. Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, Andreas Krause |
| 2018 | Fairness Through Computationally-Bounded Awareness. Michael P. Kim, Omer Reingold, Guy N. Rothblum |
| 2018 | Faithful Inversion of Generative Models for Effective Amortized Inference. Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood |
| 2018 | Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis. Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent |
| 2018 | Fast Estimation of Causal Interactions using Wold Processes. Flavio Figueiredo, Guilherme Resende Borges, Pedro O. S. Vaz de Melo, Renato M. Assunção |
| 2018 | Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity. Laming Chen, Guoxin Zhang, Eric Zhou |
| 2018 | Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang |
| 2018 | Fast Similarity Search via Optimal Sparse Lifting. Wenye Li, Jingwei Mao, Yin Zhang, Shuguang Cui |
| 2018 | Fast and Effective Robustness Certification. Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev |
| 2018 | Fast deep reinforcement learning using online adjustments from the past. Steven Hansen, Alexander Pritzel, Pablo Sprechmann, André Barreto, Charles Blundell |
| 2018 | Fast greedy algorithms for dictionary selection with generalized sparsity constraints. Kaito Fujii, Tasuku Soma |
| 2018 | FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma |
| 2018 | Faster Neural Networks Straight from JPEG. Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, Jason Yosinski |
| 2018 | Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization. Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang |
| 2018 | Fighting Boredom in Recommender Systems with Linear Reinforcement Learning. Romain Warlop, Alessandro Lazaric, Jérémie Mary |
| 2018 | First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time. Yi Xu, Rong Jin, Tianbao Yang |
| 2018 | FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction. Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang |
| 2018 | Flexible and accurate inference and learning for deep generative models. Eszter Vértes, Maneesh Sahani |
| 2018 | Flexible neural representation for physics prediction. Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Josh Tenenbaum, Daniel L. K. Yamins |
| 2018 | Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks. Bryan Lim |
| 2018 | Foreground Clustering for Joint Segmentation and Localization in Videos and Images. Abhishek Sharma |
| 2018 | Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger. Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Daniel Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier |
| 2018 | Found Graph Data and Planted Vertex Covers. Austin R. Benson, Jon M. Kleinberg |
| 2018 | Frequency-Domain Dynamic Pruning for Convolutional Neural Networks. Zhenhua Liu, Jizheng Xu, Xiulian Peng, Ruiqin Xiong |
| 2018 | From Stochastic Planning to Marginal MAP. Hao Cui, Radu Marinescu, Roni Khardon |
| 2018 | Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices. Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, Wonyong Sung |
| 2018 | Fully Understanding The Hashing Trick. Lior Kamma, Casper Benjamin Freksen, Kasper Green Larsen |
| 2018 | GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, Michael W. Mahoney |
| 2018 | GILBO: One Metric to Measure Them All. Alexander A. Alemi, Ian Fischer |
| 2018 | GLoMo: Unsupervised Learning of Transferable Relational Graphs. Zhilin Yang, Junbo Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun |
| 2018 | GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Gordon Wilson |
| 2018 | Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence. Trong Dinh Thac Do, Longbing Cao |
| 2018 | Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi |
| 2018 | Gaussian Process Conditional Density Estimation. Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth |
| 2018 | Gaussian Process Prior Variational Autoencoders. Francesco Paolo Casale, Adrian V. Dalca, Luca Saglietti, Jennifer Listgarten, Nicoló Fusi |
| 2018 | Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan |
| 2018 | Generalisation in humans and deep neural networks. Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann |
| 2018 | Generalisation of structural knowledge in the hippocampal-entorhinal system. James C. R. Whittington, Timothy H. Muller, Shirely Mark, Caswell Barry, Tim E. J. Behrens |
| 2018 | Generalization Bounds for Uniformly Stable Algorithms. Vitaly Feldman, Jan Vondrák |
| 2018 | Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. Zhilu Zhang, Mert R. Sabuncu |
| 2018 | Generalized Inverse Optimization through Online Learning. Chaosheng Dong, Yiran Chen, Bo Zeng |
| 2018 | Generalized Zero-Shot Learning with Deep Calibration Network. Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan |
| 2018 | Generalizing Graph Matching beyond Quadratic Assignment Model. Tianshu Yu, Junchi Yan, Yilin Wang, Wei Liu, Baoxin Li |
| 2018 | Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions. Boris Muzellec, Marco Cuturi |
| 2018 | Generalizing Tree Probability Estimation via Bayesian Networks. Cheng Zhang, Frederick A. Matsen IV |
| 2018 | Generalizing to Unseen Domains via Adversarial Data Augmentation. Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese |
| 2018 | Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization. Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, Bill Dolan |
| 2018 | Generative Neural Machine Translation. Harshil Shah, David Barber |
| 2018 | Generative Probabilistic Novelty Detection with Adversarial Autoencoders. Stanislav Pidhorskyi, Ranya Almohsen, Gianfranco Doretto |
| 2018 | Generative modeling for protein structures. Namrata Anand, Po-Ssu Huang |
| 2018 | Genetic-Gated Networks for Deep Reinforcement Learning. Simyung Chang, John Yang, Jaeseok Choi, Nojun Kwak |
| 2018 | Geometrically Coupled Monte Carlo Sampling. Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller |
| 2018 | Geometry Based Data Generation. Ofir Lindenbaum, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy |
| 2018 | Geometry-Aware Recurrent Neural Networks for Active Visual Recognition. Ricson Cheng, Ziyan Wang, Katerina Fragkiadaki |
| 2018 | Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu |
| 2018 | Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks. Qilong Wang, Zilin Gao, Jiangtao Xie, Wangmeng Zuo, Peihua Li |
| 2018 | Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere. Yanjun Li, Yoram Bresler |
| 2018 | Global Non-convex Optimization with Discretized Diffusions. Murat A. Erdogdu, Lester Mackey, Ohad Shamir |
| 2018 | Glow: Generative Flow with Invertible 1x1 Convolutions. Diederik P. Kingma, Prafulla Dhariwal |
| 2018 | GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training. Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander G. Schwing, Murali Annavaram, Salman Avestimehr |
| 2018 | Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation. Zhiqiang Xu |
| 2018 | Gradient Descent for Spiking Neural Networks. Dongsung Huh, Terrence J. Sejnowski |
| 2018 | Gradient Sparsification for Communication-Efficient Distributed Optimization. Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang |
| 2018 | Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay S. Pande, Jure Leskovec |
| 2018 | Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro |
| 2018 | Graphical Generative Adversarial Networks. Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang |
| 2018 | Graphical model inference: Sequential Monte Carlo meets deterministic approximations. Fredrik Lindsten, Jouni Helske, Matti Vihola |
| 2018 | Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN. Shupeng Su, Chao Zhang, Kai Han, Yonghong Tian |
| 2018 | Group Equivariant Capsule Networks. Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski |
| 2018 | GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking. Patrick H. Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh |
| 2018 | GumBolt: Extending Gumbel trick to Boltzmann priors. Amir H. Khoshaman, Mohammad H. Amin |
| 2018 | HOGWILD!-Gibbs can be PanAccurate. Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti |
| 2018 | HOUDINI: Lifelong Learning as Program Synthesis. Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri |
| 2018 | Hamiltonian Variational Auto-Encoder. Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic |
| 2018 | Hardware Conditioned Policies for Multi-Robot Transfer Learning. Tao Chen, Adithyavairavan Murali, Abhinav Gupta |
| 2018 | Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney |
| 2018 | Heterogeneous Bitwidth Binarization in Convolutional Neural Networks. Joshua Fromm, Shwetak N. Patel, Matthai Philipose |
| 2018 | Heterogeneous Multi-output Gaussian Process Prediction. Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez |
| 2018 | Hierarchical Graph Representation Learning with Differentiable Pooling. Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec |
| 2018 | Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies. Sungryull Sohn, Junhyuk Oh, Honglak Lee |
| 2018 | High Dimensional Linear Regression using Lattice Basis Reduction. Ilias Zadik, David Gamarnik |
| 2018 | HitNet: Hybrid Ternary Recurrent Neural Network. Peiqi Wang, Xinfeng Xie, Lei Deng, Guoqi Li, Dongsheng Wang, Yuan Xie |
| 2018 | Horizon-Independent Minimax Linear Regression. Alan Malek, Peter L. Bartlett |
| 2018 | How Does Batch Normalization Help Optimization? Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry |
| 2018 | How Many Samples are Needed to Estimate a Convolutional Neural Network? Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh |
| 2018 | How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? Richard Y. Zhang, Cédric Josz, Somayeh Sojoudi, Javad Lavaei |
| 2018 | How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective. Lei Wu, Chao Ma, Weinan E |
| 2018 | How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD. Zeyuan Allen-Zhu |
| 2018 | How to Start Training: The Effect of Initialization and Architecture. Boris Hanin, David Rolnick |
| 2018 | How to tell when a clustering is (approximately) correct using convex relaxations. Marina Meila |
| 2018 | Human-in-the-Loop Interpretability Prior. Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez |
| 2018 | Hunting for Discriminatory Proxies in Linear Regression Models. Samuel Yeom, Anupam Datta, Matt Fredrikson |
| 2018 | Hybrid Knowledge Routed Modules for Large-scale Object Detection. Chenhan Jiang, Hang Xu, Xiaodan Liang, Liang Lin |
| 2018 | Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks. Yingyezhe Jin, Wenrui Zhang, Peng Li |
| 2018 | Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation. Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing |
| 2018 | Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation. Jing Li, Rafal Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet |
| 2018 | Hyperbolic Neural Networks. Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann |
| 2018 | Identification and Estimation of Causal Effects from Dependent Data. Eli Sherman, Ilya Shpitser |
| 2018 | Image Inpainting via Generative Multi-column Convolutional Neural Networks. Yi Wang, Xin Tao, Xiaojuan Qi, Xiaoyong Shen, Jiaya Jia |
| 2018 | Image-to-image translation for cross-domain disentanglement. Abel Gonzalez-Garcia, Joost van de Weijer, Yoshua Bengio |
| 2018 | Implicit Bias of Gradient Descent on Linear Convolutional Networks. Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nati Srebro |
| 2018 | Implicit Probabilistic Integrators for ODEs. Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead |
| 2018 | Implicit Reparameterization Gradients. Mikhail Figurnov, Shakir Mohamed, Andriy Mnih |
| 2018 | Importance Weighting and Variational Inference. Justin Domke, Daniel Sheldon |
| 2018 | Improved Algorithms for Collaborative PAC Learning. Huy L. Nguyen, Lydia Zakynthinou |
| 2018 | Improved Expressivity Through Dendritic Neural Networks. Xundong Wu, Xiangwen Liu, Wei Li, Qing Wu |
| 2018 | Improved Network Robustness with Adversary Critic. Alexander Matyasko, Lap-Pui Chau |
| 2018 | Improving Explorability in Variational Inference with Annealed Variational Objectives. Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron C. Courville |
| 2018 | Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents. Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, Jeff Clune |
| 2018 | Improving Neural Program Synthesis with Inferred Execution Traces. Richard Shin, Illia Polosukhin, Dawn Song |
| 2018 | Improving Online Algorithms via ML Predictions. Manish Purohit, Zoya Svitkina, Ravi Kumar |
| 2018 | Improving Simple Models with Confidence Profiles. Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen |
| 2018 | Incorporating Context into Language Encoding Models for fMRI. Shailee Jain, Alexander Huth |
| 2018 | Inequity aversion improves cooperation in intertemporal social dilemmas. Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar A. Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel |
| 2018 | Inexact trust-region algorithms on Riemannian manifolds. Hiroyuki Kasai, Bamdev Mishra |
| 2018 | Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing. Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu |
| 2018 | Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo. Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes |
| 2018 | Inferring Latent Velocities from Weather Radar Data using Gaussian Processes. Rico Angell, Daniel Sheldon |
| 2018 | Inferring Networks From Random Walk-Based Node Similarities. Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis |
| 2018 | Infinite-Horizon Gaussian Processes. Arno Solin, James Hensman, Richard E. Turner |
| 2018 | Information Constraints on Auto-Encoding Variational Bayes. Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef |
| 2018 | Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces. Boyla Mainsah, Dmitry Kalika, Leslie M. Collins, Siyuan Liu, Chandra S. Throckmorton |
| 2018 | Information-theoretic Limits for Community Detection in Network Models. Chuyang Ke, Jean Honorio |
| 2018 | Informative Features for Model Comparison. Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton |
| 2018 | Insights on representational similarity in neural networks with canonical correlation. Ari S. Morcos, Maithra Raghu, Samy Bengio |
| 2018 | Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. Amir Dezfouli, Richard W. Morris, Fabio T. Ramos, Peter Dayan, Bernard W. Balleine |
| 2018 | Interactive Structure Learning with Structural Query-by-Committee. Christopher Tosh, Sanjoy Dasgupta |
| 2018 | Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. Xin Zhang, Armando Solar-Lezama, Rishabh Singh |
| 2018 | IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis. Huaibo Huang, Zhihang Li, Ran He, Zhenan Sun, Tieniu Tan |
| 2018 | Invariant Representations without Adversarial Training. Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg |
| 2018 | Invertibility of Convolutional Generative Networks from Partial Measurements. Fangchang Ma, Ulas Ayaz, Sertac Karaman |
| 2018 | Is Q-Learning Provably Efficient? Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan |
| 2018 | Isolating Sources of Disentanglement in Variational Autoencoders. Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud |
| 2018 | Iterative Value-Aware Model Learning. Amir-massoud Farahmand |
| 2018 | Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding. Hajin Shim, Sung Ju Hwang, Eunho Yang |
| 2018 | Joint Autoregressive and Hierarchical Priors for Learned Image Compression. David Minnen, Johannes Ballé, George Toderici |
| 2018 | Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution. Zhisheng Zhong, Tiancheng Shen, Yibo Yang, Zhouchen Lin, Chao Zhang |
| 2018 | KDGAN: Knowledge Distillation with Generative Adversarial Networks. Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi |
| 2018 | KONG: Kernels for ordered-neighborhood graphs. Moez Draief, Konstantin Kutzkov, Kevin Scaman, Milan Vojnovic |
| 2018 | Kalman Normalization: Normalizing Internal Representations Across Network Layers. Guangrun Wang, Jiefeng Peng, Ping Luo, Xinjiang Wang, Liang Lin |
| 2018 | Knowledge Distillation by On-the-Fly Native Ensemble. Xu Lan, Xiatian Zhu, Shaogang Gong |
| 2018 | L4: Practical loss-based stepsize adaptation for deep learning. Michal Rolínek, Georg Martius |
| 2018 | LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning. Tianyi Chen, Georgios B. Giannakis, Tao Sun, Wotao Yin |
| 2018 | LF-Net: Learning Local Features from Images. Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi |
| 2018 | Large Margin Deep Networks for Classification. Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio |
| 2018 | Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport. Théo Lacombe, Marco Cuturi, Steve Oudot |
| 2018 | Large-Scale Stochastic Sampling from the Probability Simplex. Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth |
| 2018 | Latent Alignment and Variational Attention. Yuntian Deng, Yoon Kim, Justin T. Chiu, Demi Guo, Alexander M. Rush |
| 2018 | Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments. Daniel D. Johnson, Daniel Gorelik, Ross Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit |
| 2018 | Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation. Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, Tie-Yan Liu |
| 2018 | Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor |
| 2018 | Learning Abstract Options. Matthew Riemer, Miao Liu, Gerald Tesauro |
| 2018 | Learning Attentional Communication for Multi-Agent Cooperation. Jiechuan Jiang, Zongqing Lu |
| 2018 | Learning Attractor Dynamics for Generative Memory. Yan Wu, Gregory Wayne, Karol Gregor, Timothy P. Lillicrap |
| 2018 | Learning Beam Search Policies via Imitation Learning. Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon |
| 2018 | Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels. Shahin Shahrampour, Vahid Tarokh |
| 2018 | Learning Compressed Transforms with Low Displacement Rank. Anna T. Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Ré |
| 2018 | Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra. John T. Halloran, David M. Rocke |
| 2018 | Learning Conditioned Graph Structures for Interpretable Visual Question Answering. Will Norcliffe-Brown, Stathis Vafeias, Sarah Parisot |
| 2018 | Learning Confidence Sets using Support Vector Machines. Wenbo Wang, Xingye Qiao |
| 2018 | Learning Deep Disentangled Embeddings With the F-Statistic Loss. Karl Ridgeway, Michael C. Mozer |
| 2018 | Learning Disentangled Joint Continuous and Discrete Representations. Emilien Dupont |
| 2018 | Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch |
| 2018 | Learning Hierarchical Semantic Image Manipulation through Structured Representations. Seunghoon Hong, Xinchen Yan, Thomas E. Huang, Honglak Lee |
| 2018 | Learning Invariances using the Marginal Likelihood. Mark van der Wilk, Matthias Bauer, S. T. John, James Hensman |
| 2018 | Learning Latent Subspaces in Variational Autoencoders. Jack Klys, Jake Snell, Richard S. Zemel |
| 2018 | Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction. Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum |
| 2018 | Learning Loop Invariants for Program Verification. Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song |
| 2018 | Learning Optimal Reserve Price against Non-myopic Bidders. Jinyan Liu, Zhiyi Huang, Xiangning Wang |
| 2018 | Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs. Yanlin Han, Piotr J. Gmytrasiewicz |
| 2018 | Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data. Yuanzhi Li, Yingyu Liang |
| 2018 | Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems. Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing |
| 2018 | Learning Plannable Representations with Causal InfoGAN. Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel |
| 2018 | Learning SMaLL Predictors. Vikas K. Garg, Ofer Dekel, Lin Xiao |
| 2018 | Learning Safe Policies with Expert Guidance. Jessie Huang, Fa Wu, Doina Precup, Yang Cai |
| 2018 | Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem. Victor-Emmanuel Brunel |
| 2018 | Learning Task Specifications from Demonstrations. Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit A. Seshia |
| 2018 | Learning Temporal Point Processes via Reinforcement Learning. Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song |
| 2018 | Learning To Learn Around A Common Mean. Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil |
| 2018 | Learning Versatile Filters for Efficient Convolutional Neural Networks. Yunhe Wang, Chang Xu, Chunjing Xu, Chao Xu, Dacheng Tao |
| 2018 | Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization. Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, Zhenan Sun |
| 2018 | Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders. Abubakar Abid, James Y. Zou |
| 2018 | Learning a latent manifold of odor representations from neural responses in piriform cortex. Anqi Wu, Stan L. Pashkovski, Sandeep R. Datta, Jonathan W. Pillow |
| 2018 | Learning and Inference in Hilbert Space with Quantum Graphical Models. Siddarth Srinivasan, Carlton Downey, Byron Boots |
| 2018 | Learning and Testing Causal Models with Interventions. Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy |
| 2018 | Learning convex bounds for linear quadratic control policy synthesis. Jack Umenberger, Thomas B. Schön |
| 2018 | Learning convex polytopes with margin. Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch |
| 2018 | Learning filter widths of spectral decompositions with wavelets. Haidar Khan, Bülent Yener |
| 2018 | Learning from Group Comparisons: Exploiting Higher Order Interactions. Yao Li, Minhao Cheng, Kevin Fujii, Fushing Hsieh, Cho-Jui Hsieh |
| 2018 | Learning from discriminative feature feedback. Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato |
| 2018 | Learning in Games with Lossy Feedback. Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn, Yinyu Ye |
| 2018 | Learning latent variable structured prediction models with Gaussian perturbations. Kevin Bello, Jean Honorio |
| 2018 | Learning long-range spatial dependencies with horizontal gated recurrent units. Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, Thomas Serre |
| 2018 | Learning semantic similarity in a continuous space. Michel Deudon |
| 2018 | Learning sparse neural networks via sensitivity-driven regularization. Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini |
| 2018 | Learning to Decompose and Disentangle Representations for Video Prediction. Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li Fei-Fei, Juan Carlos Niebles |
| 2018 | Learning to Exploit Stability for 3D Scene Parsing. Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman, Josh Tenenbaum, Jiajun Wu |
| 2018 | Learning to Infer Graphics Programs from Hand-Drawn Images. Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum |
| 2018 | Learning to Multitask. Yu Zhang, Ying Wei, Qiang Yang |
| 2018 | Learning to Navigate in Cities Without a Map. Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell |
| 2018 | Learning to Optimize Tensor Programs. Tianqi Chen, Lianmin Zheng, Eddie Q. Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy |
| 2018 | Learning to Play With Intrinsically-Motivated, Self-Aware Agents. Nick Haber, Damian Mrowca, Stephanie Wang, Li Fei-Fei, Daniel L. K. Yamins |
| 2018 | Learning to Reason with Third Order Tensor Products. Imanol Schlag, Jürgen Schmidhuber |
| 2018 | Learning to Reconstruct Shapes from Unseen Classes. Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Josh Tenenbaum, Bill Freeman, Jiajun Wu |
| 2018 | Learning to Repair Software Vulnerabilities with Generative Adversarial Networks. Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin |
| 2018 | Learning to Share and Hide Intentions using Information Regularization. Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab |
| 2018 | Learning to Solve SMT Formulas. Mislav Balunovic, Pavol Bielik, Martin T. Vechev |
| 2018 | Learning to Specialize with Knowledge Distillation for Visual Question Answering. Jonghwan Mun, Kimin Lee, Jinwoo Shin, Bohyung Han |
| 2018 | Learning to Teach with Dynamic Loss Functions. Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Jian-Huang Lai, Tie-Yan Liu |
| 2018 | Learning towards Minimum Hyperspherical Energy. Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song |
| 2018 | Learning with SGD and Random Features. Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco |
| 2018 | Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity. Fariborz Salehi, Ehsan Abbasi, Babak Hassibi |
| 2018 | Legendre Decomposition for Tensors. Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda |
| 2018 | Leveraged volume sampling for linear regression. Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu |
| 2018 | Leveraging the Exact Likelihood of Deep Latent Variable Models. Pierre-Alexandre Mattei, Jes Frellsen |
| 2018 | Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins |
| 2018 | Lifelong Inverse Reinforcement Learning. Jorge A. Mendez, Shashank Shivkumar, Eric Eaton |
| 2018 | Lifted Weighted Mini-Bucket. Nicholas Gallo, Alexander Ihler |
| 2018 | Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives. Song Zhou, Swati Gupta, Madeleine Udell |
| 2018 | Link Prediction Based on Graph Neural Networks. Muhan Zhang, Yixin Chen |
| 2018 | LinkNet: Relational Embedding for Scene Graph. Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon |
| 2018 | Lipschitz regularity of deep neural networks: analysis and efficient estimation. Aladin Virmaux, Kevin Scaman |
| 2018 | Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama |
| 2018 | Local Differential Privacy for Evolving Data. Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner |
| 2018 | Long short-term memory and Learning-to-learn in networks of spiking neurons. Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass |
| 2018 | Loss Functions for Multiset Prediction. Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho |
| 2018 | Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson |
| 2018 | Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch. Osman Asif Malik, Stephen Becker |
| 2018 | Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames. Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines |
| 2018 | Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks. Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang |
| 2018 | M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search. Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao |
| 2018 | MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval. Helena Peic Tukuljac, Antoine Deleforge, Rémi Gribonval |
| 2018 | MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models. Boyuan Pan, Yazheng Yang, Hao Li, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He |
| 2018 | Mallows Models for Top-k Lists. Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi |
| 2018 | Manifold Structured Prediction. Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco |
| 2018 | Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks. Anirvan M. Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri B. Chklovskii |
| 2018 | Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction. Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson |
| 2018 | Masking: A New Perspective of Noisy Supervision. Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama |
| 2018 | Maximizing Induced Cardinality Under a Determinantal Point Process. Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet |
| 2018 | Maximizing acquisition functions for Bayesian optimization. James T. Wilson, Frank Hutter, Marc Peter Deisenroth |
| 2018 | Maximum Causal Tsallis Entropy Imitation Learning. Kyungjae Lee, Sungjoon Choi, Songhwai Oh |
| 2018 | Maximum-Entropy Fine Grained Classification. Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik |
| 2018 | Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues. Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan |
| 2018 | Mean-field theory of graph neural networks in graph partitioning. Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi |
| 2018 | Measures of distortion for machine learning. Leena Chennuru Vankadara, Ulrike von Luxburg |
| 2018 | Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing. Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao |
| 2018 | Memory Replay GANs: Learning to Generate New Categories without Forgetting. Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, Bogdan Raducanu |
| 2018 | Mental Sampling in Multimodal Representations. Jian-Qiao Zhu, Adam Sanborn, Nick Chater |
| 2018 | Mesh-TensorFlow: Deep Learning for Supercomputers. Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman |
| 2018 | Meta-Gradient Reinforcement Learning. Zhongwen Xu, Hado van Hasselt, David Silver |
| 2018 | Meta-Learning MCMC Proposals. Tongzhou Wang, Yi Wu, Dave Moore, Stuart J. Russell |
| 2018 | Meta-Reinforcement Learning of Structured Exploration Strategies. Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine |
| 2018 | MetaAnchor: Learning to Detect Objects with Customized Anchors. Tong Yang, Xiangyu Zhang, Zeming Li, Wenqiang Zhang, Jian Sun |
| 2018 | MetaGAN: An Adversarial Approach to Few-Shot Learning. Ruixiang Zhang, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song |
| 2018 | MetaReg: Towards Domain Generalization using Meta-Regularization. Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa |
| 2018 | Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators. Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara |
| 2018 | MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare. Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun |
| 2018 | Middle-Out Decoding. Shikib Mehri, Leonid Sigal |
| 2018 | Minimax Estimation of Neural Net Distance. Kaiyi Ji, Yingbin Liang |
| 2018 | Minimax Statistical Learning with Wasserstein distances. Jaeho Lee, Maxim Raginsky |
| 2018 | Mirrored Langevin Dynamics. Ya-Ping Hsieh, Ali Kavis, Paul Rolland, Volkan Cevher |
| 2018 | MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin |
| 2018 | Mixture Matrix Completion. Daniel L. Pimentel-Alarcón |
| 2018 | Model Agnostic Supervised Local Explanations. Gregory Plumb, Denali Molitor, Ameet Talwalkar |
| 2018 | Model-Agnostic Private Learning. Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar |
| 2018 | Model-based targeted dimensionality reduction for neuronal population data. Mikio C. Aoi, Jonathan W. Pillow |
| 2018 | Modeling Dynamic Missingness of Implicit Feedback for Recommendation. Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang |
| 2018 | Modelling and unsupervised learning of symmetric deformable object categories. James Thewlis, Hakan Bilen, Andrea Vedaldi |
| 2018 | Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data. Xenia Miscouridou, Francois Caron, Yee Whye Teh |
| 2018 | Modern Neural Networks Generalize on Small Data Sets. Matthew Olson, Abraham J. Wyner, Richard Berk |
| 2018 | Modular Networks: Learning to Decompose Neural Computation. Louis Kirsch, Julius Kunze, David Barber |
| 2018 | Monte-Carlo Tree Search for Constrained POMDPs. Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, Kee-Eung Kim |
| 2018 | Moonshine: Distilling with Cheap Convolutions. Elliot J. Crowley, Gavin Gray, Amos J. Storkey |
| 2018 | Multi-Agent Generative Adversarial Imitation Learning. Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon |
| 2018 | Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization. Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong |
| 2018 | Multi-Class Learning: From Theory to Algorithm. Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang |
| 2018 | Multi-Layered Gradient Boosting Decision Trees. Ji Feng, Yang Yu, Zhi-Hua Zhou |
| 2018 | Multi-Task Learning as Multi-Objective Optimization. Ozan Sener, Vladlen Koltun |
| 2018 | Multi-Task Zipping via Layer-wise Neuron Sharing. Xiaoxi He, Zimu Zhou, Lothar Thiele |
| 2018 | Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation. Edward J. Smith, Scott Fujimoto, David Meger |
| 2018 | Multi-armed Bandits with Compensation. Siwei Wang, Longbo Huang |
| 2018 | Multi-domain Causal Structure Learning in Linear Systems. AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang |
| 2018 | Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint. Rajan Udwani |
| 2018 | Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations. Tong Wang |
| 2018 | Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages. Michelle Yuan, Benjamin Van Durme, Jordan L. Ying |
| 2018 | Multimodal Generative Models for Scalable Weakly-Supervised Learning. Mike Wu, Noah D. Goodman |
| 2018 | Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices. Don Kurian Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, Prateek Jain |
| 2018 | Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning. Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor |
| 2018 | Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games. Yun Kuen Cheung |
| 2018 | Multitask Boosting for Survival Analysis with Competing Risks. Alexis Bellot, Mihaela van der Schaar |
| 2018 | Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. Tom Dupré la Tour, Thomas Moreau, Mainak Jas, Alexandre Gramfort |
| 2018 | Multivariate Time Series Imputation with Generative Adversarial Networks. Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, Xiaojie Yuan |
| 2018 | NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations. Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino J. Gomez |
| 2018 | NEON2: Finding Local Minima via First-Order Oracles. Zeyuan Allen-Zhu, Yuanzhi Li |
| 2018 | Natasha 2: Faster Non-Convex Optimization Than SGD. Zeyuan Allen-Zhu |
| 2018 | Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models. Minjia Zhang, Wenhan Wang, Xiaodong Liu, Jianfeng Gao, Yuxiong He |
| 2018 | Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes. Ronan Fruit, Matteo Pirotta, Alessandro Lazaric |
| 2018 | Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models. Yining Wang, Xi Chen, Yuan Zhou |
| 2018 | Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, Yinyu Ye |
| 2018 | Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan |
| 2018 | Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making. Nishant Desai, Andrew Critch, Stuart J. Russell |
| 2018 | Neighbourhood Consensus Networks. Ignacio Rocco, Mircea Cimpoi, Relja Arandjelovic, Akihiko Torii, Tomás Pajdla, Josef Sivic |
| 2018 | Neural Architecture Optimization. Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu |
| 2018 | Neural Architecture Search with Bayesian Optimisation and Optimal Transport. Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing |
| 2018 | Neural Arithmetic Logic Units. Andrew Trask, Felix Hill, Scott E. Reed, Jack W. Rae, Chris Dyer, Phil Blunsom |
| 2018 | Neural Code Comprehension: A Learnable Representation of Code Semantics. Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler |
| 2018 | Neural Edit Operations for Biological Sequences. Satoshi Koide, Keisuke Kawano, Takuro Kutsuna |
| 2018 | Neural Guided Constraint Logic Programming for Program Synthesis. Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel |
| 2018 | Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability. Michael Tsang, Hanpeng Liu, Sanjay Purushotham, Pavankumar Murali, Yan Liu |
| 2018 | Neural Nearest Neighbors Networks. Tobias Plötz, Stefan Roth |
| 2018 | Neural Networks Trained to Solve Differential Equations Learn General Representations. Martin Magill, Faisal Z. Qureshi, Hendrick W. de Haan |
| 2018 | Neural Ordinary Differential Equations. Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud |
| 2018 | Neural Proximal Gradient Descent for Compressive Imaging. Morteza Mardani, Qingyun Sun, David L. Donoho, Vardan Papyan, Hatef Monajemi, Shreyas Vasanawala, John M. Pauly |
| 2018 | Neural Tangent Kernel: Convergence and Generalization in Neural Networks. Arthur Jacot, Clément Hongler, Franck Gabriel |
| 2018 | Neural Voice Cloning with a Few Samples. Sercan Ömer Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou |
| 2018 | Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Josh Tenenbaum |
| 2018 | New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity. Pan Zhou, Xiaotong Yuan, Jiashi Feng |
| 2018 | Non-Adversarial Mapping with VAEs. Yedid Hoshen |
| 2018 | Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates. Quoc Tran-Dinh |
| 2018 | Non-Local Recurrent Network for Image Restoration. Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, Thomas S. Huang |
| 2018 | Non-delusional Q-learning and value-iteration. Tyler Lu, Dale Schuurmans, Craig Boutilier |
| 2018 | Non-metric Similarity Graphs for Maximum Inner Product Search. Stanislav Morozov, Artem Babenko |
| 2018 | Non-monotone Submodular Maximization in Exponentially Fewer Iterations. Eric Balkanski, Adam Breuer, Yaron Singer |
| 2018 | Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling. Yunzhe Tao, Qi Sun, Qiang Du, Wei Liu |
| 2018 | Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks. Quan Zhang, Mingyuan Zhou |
| 2018 | Nonparametric Density Estimation under Adversarial Losses. Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos |
| 2018 | Nonparametric learning from Bayesian models with randomized objective functions. Simon Lyddon, Stephen Walker, Chris C. Holmes |
| 2018 | Norm matters: efficient and accurate normalization schemes in deep networks. Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry |
| 2018 | Norm-Ranging LSH for Maximum Inner Product Search. Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng |
| 2018 | Object-Oriented Dynamics Predictor. Guangxiang Zhu, Zhiao Huang, Chongjie Zhang |
| 2018 | Objective and efficient inference for couplings in neuronal networks. Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima |
| 2018 | Occam's razor is insufficient to infer the preferences of irrational agents. Stuart Armstrong, Sören Mindermann |
| 2018 | On Binary Classification in Extreme Regions. Hamid Jalalzai, Stéphan Clémençon, Anne Sabourin |
| 2018 | On Controllable Sparse Alternatives to Softmax. Anirban Laha, Saneem Ahmed Chemmengath, Priyanka Agrawal, Mitesh M. Khapra, Karthik Sankaranarayanan, Harish G. Ramaswamy |
| 2018 | On Coresets for Logistic Regression. Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff |
| 2018 | On Fast Leverage Score Sampling and Optimal Learning. Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco |
| 2018 | On GANs and GMMs. Eitan Richardson, Yair Weiss |
| 2018 | On Learning Intrinsic Rewards for Policy Gradient Methods. Zeyu Zheng, Junhyuk Oh, Satinder Singh |
| 2018 | On Learning Markov Chains. Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati |
| 2018 | On Markov Chain Gradient Descent. Tao Sun, Yuejiao Sun, Wotao Yin |
| 2018 | On Misinformation Containment in Online Social Networks. Guangmo Amo Tong, Ding-Zhu Du, Weili Wu |
| 2018 | On Neuronal Capacity. Pierre Baldi, Roman Vershynin |
| 2018 | On Oracle-Efficient PAC RL with Rich Observations. Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire |
| 2018 | On gradient regularizers for MMD GANs. Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton |
| 2018 | On preserving non-discrimination when combining expert advice. Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro |
| 2018 | On the Convergence and Robustness of Training GANs with Regularized Optimal Transport. Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee |
| 2018 | On the Dimensionality of Word Embedding. Zi Yin, Yuanyuan Shen |
| 2018 | On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport. Lénaïc Chizat, Francis R. Bach |
| 2018 | On the Local Hessian in Back-propagation. Huishuai Zhang, Wei Chen, Tie-Yan Liu |
| 2018 | On the Local Minima of the Empirical Risk. Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan |
| 2018 | One-Shot Unsupervised Cross Domain Translation. Sagie Benaim, Lior Wolf |
| 2018 | Online Adaptive Methods, Universality and Acceleration. Kfir Yehuda Levy, Alp Yurtsever, Volkan Cevher |
| 2018 | Online Improper Learning with an Approximation Oracle. Elad Hazan, Wei Hu, Yuanzhi Li, Zhiyuan Li |
| 2018 | Online Learning of Quantum States. Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak |
| 2018 | Online Learning with an Unknown Fairness Metric. Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth |
| 2018 | Online Reciprocal Recommendation with Theoretical Performance Guarantees. Claudio Gentile, Nikos Parotsidis, Fabio Vitale |
| 2018 | Online Robust Policy Learning in the Presence of Unknown Adversaries. Aaron J. Havens, Zhanhong Jiang, Soumik Sarkar |
| 2018 | Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks. Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias |
| 2018 | Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. Hippolyt Ritter, Aleksandar Botev, David Barber |
| 2018 | Online convex optimization for cumulative constraints. Jianjun Yuan, Andrew G. Lamperski |
| 2018 | Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization. Rad Niazadeh, Tim Roughgarden, Joshua R. Wang |
| 2018 | Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee |
| 2018 | Optimal Subsampling with Influence Functions. Daniel Ting, Eric Brochu |
| 2018 | Optimistic optimization of a Brownian. Jean-Bastien Grill, Michal Valko, Rémi Munos |
| 2018 | Optimization for Approximate Submodularity. Yaron Singer, Avinatan Hassidim |
| 2018 | Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates. Yining Wang, Sivaraman Balakrishnan, Aarti Singh |
| 2018 | Optimization over Continuous and Multi-dimensional Decisions with Observational Data. Dimitris Bertsimas, Christopher McCord |
| 2018 | Orthogonally Decoupled Variational Gaussian Processes. Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth |
| 2018 | Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering. Medhini Narasimhan, Svetlana Lazebnik, Alexander G. Schwing |
| 2018 | Out-of-Distribution Detection using Multiple Semantic Label Representations. Gabi Shalev, Yossi Adi, Joseph Keshet |
| 2018 | Overcoming Language Priors in Visual Question Answering with Adversarial Regularization. Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee |
| 2018 | Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. Mikhail Belkin, Daniel J. Hsu, Partha Mitra |
| 2018 | Overlapping Clustering Models, and One (class) SVM to Bind Them All. Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti |
| 2018 | PAC-Bayes Tree: weighted subtrees with guarantees. Tin D. Nguyen, Samory Kpotufe |
| 2018 | PAC-Bayes bounds for stable algorithms with instance-dependent priors. Omar Rivasplata, Csaba Szepesvári, John Shawe-Taylor, Emilio Parrado-Hernández, Shiliang Sun |
| 2018 | PAC-learning in the presence of adversaries. Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal |
| 2018 | PCA of high dimensional random walks with comparison to neural network training. Joseph M. Antognini, Jascha Sohl-Dickstein |
| 2018 | PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits. Bianca Dumitrascu, Karen Feng, Barbara E. Engelhardt |
| 2018 | PacGAN: The power of two samples in generative adversarial networks. Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh |
| 2018 | Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. Grant M. Rotskoff, Eric Vanden-Eijnden |
| 2018 | Paraphrasing Complex Network: Network Compression via Factor Transfer. Jangho Kim, Seonguk Park, Nojun Kwak |
| 2018 | Parsimonious Bayesian deep networks. Mingyuan Zhou |
| 2018 | Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning. Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, Qi Wu |
| 2018 | Partially-Supervised Image Captioning. Peter Anderson, Stephen Gould, Mark Johnson |
| 2018 | Pelee: A Real-Time Object Detection System on Mobile Devices. Robert J. Wang, Xiang Li, Charles X. Ling |
| 2018 | Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams. Tam Le, Makoto Yamada |
| 2018 | Phase Retrieval Under a Generative Prior. Paul Hand, Oscar Leong, Vladislav Voroninski |
| 2018 | Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training. Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, Alexander G. Schwing |
| 2018 | Playing hard exploration games by watching YouTube. Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas |
| 2018 | Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis. Alyson K. Fletcher, Parthe Pandit, Sundeep Rangan, Subrata Sarkar, Philip Schniter |
| 2018 | Point process latent variable models of larval zebrafish behavior. Anuj Sharma, Robert Johnson, Florian Engert, Scott W. Linderman |
| 2018 | PointCNN: Convolution On X-Transformed Points. Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, Baoquan Chen |
| 2018 | Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks. Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein |
| 2018 | Policy Optimization via Importance Sampling. Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli |
| 2018 | Policy Regret in Repeated Games. Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri |
| 2018 | Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. Andrea Tirinzoni, Marek Petrik, Xiangli Chen, Brian D. Ziebart |
| 2018 | Porcupine Neural Networks: Approximating Neural Network Landscapes. Soheil Feizi, Hamid Javadi, Jesse M. Zhang, David Tse |
| 2018 | Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization. Yuanxiang Gao, Li Chen, Baochun Li |
| 2018 | Posterior Concentration for Sparse Deep Learning. Veronika Rocková, Nicholas Polson |
| 2018 | Power-law efficient neural codes provide general link between perceptual bias and discriminability. Michael J. Morais, Jonathan W. Pillow |
| 2018 | Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching. Stepan Tulyakov, Anton Ivanov, François Fleuret |
| 2018 | Practical Methods for Graph Two-Sample Testing. Debarghya Ghoshdastidar, Ulrike von Luxburg |
| 2018 | Practical exact algorithm for trembling-hand equilibrium refinements in games. Gabriele Farina, Nicola Gatti, Tuomas Sandholm |
| 2018 | Precision and Recall for Time Series. Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich |
| 2018 | Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. David Madras, Toniann Pitassi, Richard S. Zemel |
| 2018 | Predictive Approximate Bayesian Computation via Saddle Points. Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He |
| 2018 | Predictive Uncertainty Estimation via Prior Networks. Andrey Malinin, Mark J. F. Gales |
| 2018 | Preference Based Adaptation for Learning Objectives. Yao-Xiang Ding, Zhi-Hua Zhou |
| 2018 | Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences. Borja Balle, Gilles Barthe, Marco Gaboardi |
| 2018 | Probabilistic Matrix Factorization for Automated Machine Learning. Nicoló Fusi, Rishit Sheth, Melih Elibol |
| 2018 | Probabilistic Model-Agnostic Meta-Learning. Chelsea Finn, Kelvin Xu, Sergey Levine |
| 2018 | Probabilistic Neural Programmed Networks for Scene Generation. Zhiwei Deng, Jiacheng Chen, Yifang Fu, Greg Mori |
| 2018 | Processing of missing data by neural networks. Marek Smieja, Lukasz Struski, Jacek Tabor, Bartosz Zielinski, Przemyslaw Spurek |
| 2018 | Provable Gaussian Embedding with One Observation. Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang |
| 2018 | Provable Variational Inference for Constrained Log-Submodular Models. Josip Djolonga, Stefanie Jegelka, Andreas Krause |
| 2018 | Provably Correct Automatic Sub-Differentiation for Qualified Programs. Sham M. Kakade, Jason D. Lee |
| 2018 | Proximal Graphical Event Models. Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao |
| 2018 | Proximal SCOPE for Distributed Sparse Learning. Shen-Yi Zhao, Gong-Duo Zhang, Ming-Wei Li, Wu-Jun Li |
| 2018 | Q-learning with Nearest Neighbors. Devavrat Shah, Qiaomin Xie |
| 2018 | Quadratic Decomposable Submodular Function Minimization. Pan Li, Niao He, Olgica Milenkovic |
| 2018 | Quadrature-based features for kernel approximation. Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan V. Oseledets |
| 2018 | Quantifying Learning Guarantees for Convex but Inconsistent Surrogates. Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin |
| 2018 | Query Complexity of Bayesian Private Learning. Kuang Xu |
| 2018 | Query K-means Clustering and the Double Dixie Cup Problem. I (Eli) Chien, Chao Pan, Olgica Milenkovic |
| 2018 | REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis. Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, Edward Y. Chang |
| 2018 | Random Feature Stein Discrepancies. Jonathan H. Huggins, Lester Mackey |
| 2018 | Randomized Prior Functions for Deep Reinforcement Learning. Ian Osband, John Aslanides, Albin Cassirer |
| 2018 | Re-evaluating evaluation. David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel |
| 2018 | Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. Avital Oliver, Augustus Odena, Colin Raffel, Ekin Dogus Cubuk, Ian J. Goodfellow |
| 2018 | Rectangular Bounding Process. Xuhui Fan, Bin Li, Scott A. Sisson |
| 2018 | Recurrent Relational Networks. Rasmus Berg Palm, Ulrich Paquet, Ole Winther |
| 2018 | Recurrent Transformer Networks for Semantic Correspondence. Seungryong Kim, Stephen Lin, Sangryul Jeon, Dongbo Min, Kwanghoon Sohn |
| 2018 | Recurrent World Models Facilitate Policy Evolution. David Ha, Jürgen Schmidhuber |
| 2018 | Recurrently Controlled Recurrent Networks. Yi Tay, Anh Tuan Luu, Siu Cheung Hui |
| 2018 | Reducing Network Agnostophobia. Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult |
| 2018 | Regret Bounds for Online Portfolio Selection with a Cardinality Constraint. Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi |
| 2018 | Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator. Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu |
| 2018 | Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior. Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling |
| 2018 | Regularization Learning Networks: Deep Learning for Tabular Datasets. Ira Shavitt, Eran Segal |
| 2018 | Regularizing by the Variance of the Activations' Sample-Variances. Etai Littwin, Lior Wolf |
| 2018 | Reinforced Continual Learning. Ju Xu, Zhanxing Zhu |
| 2018 | Reinforcement Learning for Solving the Vehicle Routing Problem. MohammadReza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takác |
| 2018 | Reinforcement Learning of Theorem Proving. Cezary Kaliszyk, Josef Urban, Henryk Michalewski, Miroslav Olsák |
| 2018 | Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach. Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee |
| 2018 | Relating Leverage Scores and Density using Regularized Christoffel Functions. Edouard Pauwels, Francis R. Bach, Jean-Philippe Vert |
| 2018 | Relational recurrent neural networks. Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap |
| 2018 | Removing Hidden Confounding by Experimental Grounding. Nathan Kallus, Aahlad Manas Puli, Uri Shalit |
| 2018 | Removing the Feature Correlation Effect of Multiplicative Noise. Zijun Zhang, Yining Zhang, Zongpeng Li |
| 2018 | RenderNet: A deep convolutional network for differentiable rendering from 3D shapes. Thu Nguyen-Phuoc, Chuan Li, Stephen Balaban, Yong-Liang Yang |
| 2018 | Reparameterization Gradient for Non-differentiable Models. Wonyeol Lee, Hangyeol Yu, Hongseok Yang |
| 2018 | Representation Balancing MDPs for Off-policy Policy Evaluation. Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill |
| 2018 | Representation Learning for Treatment Effect Estimation from Observational Data. Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, Aidong Zhang |
| 2018 | Representation Learning of Compositional Data. Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun |
| 2018 | Representer Point Selection for Explaining Deep Neural Networks. Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar |
| 2018 | ResNet with one-neuron hidden layers is a Universal Approximator. Hongzhou Lin, Stefanie Jegelka |
| 2018 | Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes. Junqi Tang, Mohammad Golbabaee, Francis R. Bach, Mike E. Davies |
| 2018 | RetGK: Graph Kernels based on Return Probabilities of Random Walks. Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, Arye Nehorai |
| 2018 | Reversible Recurrent Neural Networks. Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B. Grosse |
| 2018 | Revisiting (\epsilon, \gamma, \tau)-similarity learning for domain adaptation. Sofiane Dhouib, Ievgen Redko |
| 2018 | Revisiting Decomposable Submodular Function Minimization with Incidence Relations. Pan Li, Olgica Milenkovic |
| 2018 | Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection. Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord |
| 2018 | Reward learning from human preferences and demonstrations in Atari. Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei |
| 2018 | Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling. Shannon R. McCurdy |
| 2018 | Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias. Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi, Lerrel Pinto |
| 2018 | Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks. Zhihao Zheng, Pengyu Hong |
| 2018 | Robust Hypothesis Testing Using Wasserstein Uncertainty Sets. Rui Gao, Liyan Xie, Yao Xie, Huan Xu |
| 2018 | Robust Learning of Fixed-Structure Bayesian Networks. Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart |
| 2018 | Robust Subspace Approximation in a Stream. Roie Levin, Anish Prasad Sevekari, David P. Woodruff |
| 2018 | Robustness of conditional GANs to noisy labels. Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh |
| 2018 | SEGA: Variance Reduction via Gradient Sketching. Filip Hanzely, Konstantin Mishchenko, Peter Richtárik |
| 2018 | SING: Symbol-to-Instrument Neural Generator. Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Léon Bottou, Francis R. Bach |
| 2018 | SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan |
| 2018 | SLAYER: Spike Layer Error Reassignment in Time. Sumit Bam Shrestha, Garrick Orchard |
| 2018 | SNIPER: Efficient Multi-Scale Training. Bharat Singh, Mahyar Najibi, Larry S. Davis |
| 2018 | SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang |
| 2018 | Safe Active Learning for Time-Series Modeling with Gaussian Processes. Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong |
| 2018 | Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation. Tomoya Murata, Taiji Suzuki |
| 2018 | Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion. Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee |
| 2018 | Sanity Checks for Saliency Maps. Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim |
| 2018 | Scalable Coordinated Exploration in Concurrent Reinforcement Learning. Maria Dimakopoulou, Ian Osband, Benjamin Van Roy |
| 2018 | Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, John C. Duchi |
| 2018 | Scalable Hyperparameter Transfer Learning. Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau |
| 2018 | Scalable Laplacian K-modes. Imtiaz Masud Ziko, Eric Granger, Ismail Ben Ayed |
| 2018 | Scalable Robust Matrix Factorization with Nonconvex Loss. Quanming Yao, James T. Kwok |
| 2018 | Scalable methods for 8-bit training of neural networks. Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry |
| 2018 | Scalar Posterior Sampling with Applications. Georgios Theocharous, Zheng Wen, Yasin Abbasi, Nikos Vlassis |
| 2018 | Scaling Gaussian Process Regression with Derivatives. David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson |
| 2018 | Scaling provable adversarial defenses. Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter |
| 2018 | Scaling the Poisson GLM to massive neural datasets through polynomial approximations. David M. Zoltowski, Jonathan W. Pillow |
| 2018 | Searching for Efficient Multi-Scale Architectures for Dense Image Prediction. Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens |
| 2018 | See and Think: Disentangling Semantic Scene Completion. Shice Liu, Yu Hu, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, Xiaowei Li |
| 2018 | Self-Erasing Network for Integral Object Attention. Qibin Hou, Peng-Tao Jiang, Yunchao Wei, Ming-Ming Cheng |
| 2018 | Self-Supervised Generation of Spatial Audio for 360° Video. Pedro Morgado, Nuno Vasconcelos, Timothy R. Langlois, Oliver Wang |
| 2018 | Semi-Supervised Learning with Declaratively Specified Entropy Constraints. Haitian Sun, William W. Cohen, Lidong Bing |
| 2018 | Semi-crowdsourced Clustering with Deep Generative Models. Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang |
| 2018 | Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. Neal Jean, Sang Michael Xie, Stefano Ermon |
| 2018 | Semidefinite relaxations for certifying robustness to adversarial examples. Aditi Raghunathan, Jacob Steinhardt, Percy Liang |
| 2018 | Sequence-to-Segment Networks for Segment Detection. Zijun Wei, Boyu Wang, Minh Hoai Nguyen, Jianming Zhang, Zhe Lin, Xiaohui Shen, Radomír Mech, Dimitris Samaras |
| 2018 | Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects. Adam R. Kosiorek, Hyunjik Kim, Yee Whye Teh, Ingmar Posner |
| 2018 | Sequential Context Encoding for Duplicate Removal. Lu Qi, Shu Liu, Jianping Shi, Jiaya Jia |
| 2018 | Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling. Emilie Kaufmann, Wouter M. Koolen, Aurélien Garivier |
| 2018 | Sharp Bounds for Generalized Uniformity Testing. Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2018 | Sigsoftmax: Reanalysis of the Softmax Bottleneck. Sekitoshi Kanai, Yasuhiro Fujiwara, Yuki Yamanaka, Shuichi Adachi |
| 2018 | SimplE Embedding for Link Prediction in Knowledge Graphs. Seyed Mehran Kazemi, David Poole |
| 2018 | Simple random search of static linear policies is competitive for reinforcement learning. Horia Mania, Aurelia Guy, Benjamin Recht |
| 2018 | Simple, Distributed, and Accelerated Probabilistic Programming. Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul |
| 2018 | Single-Agent Policy Tree Search With Guarantees. Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber |
| 2018 | Size-Noise Tradeoffs in Generative Networks. Bolton Bailey, Matus Telgarsky |
| 2018 | Sketching Method for Large Scale Combinatorial Inference. Wei Sun, Junwei Lu, Han Liu |
| 2018 | Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons. Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos H. Papadimitriou, Amin Saberi, Santosh S. Vempala |
| 2018 | Smoothed analysis of the low-rank approach for smooth semidefinite programs. Thomas Pumir, Samy Jelassi, Nicolas Boumal |
| 2018 | Snap ML: A Hierarchical Framework for Machine Learning. Celestine Dünner, Thomas P. Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Gummadi Ravi, Madhusudanan Kandasamy, Haralampos Pozidis |
| 2018 | Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis. Haoye Dong, Xiaodan Liang, Ke Gong, Hanjiang Lai, Jia Zhu, Jian Yin |
| 2018 | Solving Large Sequential Games with the Excessive Gap Technique. Christian Kroer, Gabriele Farina, Tuomas Sandholm |
| 2018 | Solving Non-smooth Constrained Programs with Lower Complexity than \mathcal{O}(1/\varepsilon): A Primal-Dual Homotopy Smoothing Approach. Xiaohan Wei, Hao Yu, Qing Ling, Michael J. Neely |
| 2018 | Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio |
| 2018 | Sparse Covariance Modeling in High Dimensions with Gaussian Processes. Rui Li, Kishan KC, Feng Cui, Justin Domke, Anne R. Haake |
| 2018 | Sparse DNNs with Improved Adversarial Robustness. Yiwen Guo, Chao Zhang, Changshui Zhang, Yurong Chen |
| 2018 | Sparse PCA from Sparse Linear Regression. Guy Bresler, Sung Min Park, Madalina Persu |
| 2018 | Sparsified SGD with Memory. Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi |
| 2018 | Speaker-Follower Models for Vision-and-Language Navigation. Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, Trevor Darrell |
| 2018 | Spectral Filtering for General Linear Dynamical Systems. Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang |
| 2018 | Spectral Signatures in Backdoor Attacks. Brandon Tran, Jerry Li, Aleksander Madry |
| 2018 | SplineNets: Continuous Neural Decision Graphs. Cem Keskin, Shahram Izadi |
| 2018 | Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning. Yunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei (Mark) Zhang |
| 2018 | Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes. Loucas Pillaud-Vivien, Alessandro Rudi, Francis R. Bach |
| 2018 | Statistical and Computational Trade-Offs in Kernel K-Means. Daniele Calandriello, Lorenzo Rosasco |
| 2018 | Statistical mechanics of low-rank tensor decomposition. Jonathan Kadmon, Surya Ganguli |
| 2018 | Stein Variational Gradient Descent as Moment Matching. Qiang Liu, Dilin Wang |
| 2018 | Step Size Matters in Deep Learning. Kamil Nar, Shankar Sastry |
| 2018 | Stimulus domain transfer in recurrent models for large scale cortical population prediction on video. Fabian H. Sinz, Alexander S. Ecker, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, Emmanouil Froudarakis, Dimitri Yatsenko, Xaq Pitkow, Jacob Reimer, Andreas S. Tolias |
| 2018 | Stochastic Chebyshev Gradient Descent for Spectral Optimization. Insu Han, Haim Avron, Jinwoo Shin |
| 2018 | Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities. Yunwen Lei, Ke Tang |
| 2018 | Stochastic Cubic Regularization for Fast Nonconvex Optimization. Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan |
| 2018 | Stochastic Expectation Maximization with Variance Reduction. Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang |
| 2018 | Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. Dongruo Zhou, Pan Xu, Quanquan Gu |
| 2018 | Stochastic Nonparametric Event-Tensor Decomposition. Shandian Zhe, Yishuai Du |
| 2018 | Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity. Conghui Tan, Tong Zhang, Shiqian Ma, Ji Liu |
| 2018 | Stochastic Spectral and Conjugate Descent Methods. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov |
| 2018 | Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features. Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora |
| 2018 | Streamlining Variational Inference for Constraint Satisfaction Problems. Aditya Grover, Tudor Achim, Stefano Ermon |
| 2018 | Structural Causal Bandits: Where to Intervene? Sanghack Lee, Elias Bareinboim |
| 2018 | Structure-Aware Convolutional Neural Networks. Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan |
| 2018 | Structured Local Minima in Sparse Blind Deconvolution. Yuqian Zhang, Han-Wen Kuo, John Wright |
| 2018 | Sublinear Time Low-Rank Approximation of Distance Matrices. Ainesh Bakshi, David P. Woodruff |
| 2018 | Submodular Field Grammars: Representation, Inference, and Application to Image Parsing. Abram L. Friesen, Pedro M. Domingos |
| 2018 | Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions. Wenruo Bai, William Stafford Noble, Jeff A. Bilmes |
| 2018 | Supervised autoencoders: Improving generalization performance with unsupervised regularizers. Lei Le, Andrew Patterson, Martha White |
| 2018 | Supervising Unsupervised Learning. Vikas K. Garg |
| 2018 | Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli |
| 2018 | Symbolic Graph Reasoning Meets Convolutions. Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing |
| 2018 | Synaptic Strength For Convolutional Neural Network. Chen Lin, Zhao Zhong, Wei Wu, Junjie Yan |
| 2018 | Synthesize Policies for Transfer and Adaptation across Tasks and Environments. Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha |
| 2018 | TADAM: Task dependent adaptive metric for improved few-shot learning. Boris N. Oreshkin, Pau Rodríguez López, Alexandre Lacoste |
| 2018 | TETRIS: TilE-matching the TRemendous Irregular Sparsity. Yu Ji, Ling Liang, Lei Deng, Youyang Zhang, Youhui Zhang, Yuan Xie |
| 2018 | Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming. Bart van Merrienboer, Dan Moldovan, Alexander B. Wiltschko |
| 2018 | Task-Driven Convolutional Recurrent Models of the Visual System. Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins |
| 2018 | Teaching Inverse Reinforcement Learners via Features and Demonstrations. Luis Haug, Sebastian Tschiatschek, Adish Singla |
| 2018 | Temporal Regularization for Markov Decision Process. Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup |
| 2018 | Temporal alignment and latent Gaussian process factor inference in population spike trains. Lea Duncker, Maneesh Sahani |
| 2018 | Testing for Families of Distributions via the Fourier Transform. Alistair Stewart, Ilias Diakonikolas, Clément L. Canonne |
| 2018 | Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language. Seonghyeon Nam, Yunji Kim, Seon Joo Kim |
| 2018 | The Cluster Description Problem - Complexity Results, Formulations and Approximations. Ian Davidson, Antoine Gourru, S. S. Ravi |
| 2018 | The Convergence of Sparsified Gradient Methods. Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cédric Renggli |
| 2018 | The Description Length of Deep Learning models. Léonard Blier, Yann Ollivier |
| 2018 | The Effect of Network Width on the Performance of Large-batch Training. Lingjiao Chen, Hongyi Wang, Jinman Zhao, Dimitris S. Papailiopoulos, Paraschos Koutris |
| 2018 | The Everlasting Database: Statistical Validity at a Fair Price. Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro |
| 2018 | The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation. Zi Yin, Vin Sachidananda, Balaji Prabhakar |
| 2018 | The Importance of Sampling inMeta-Reinforcement Learning. Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever |
| 2018 | The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization. Constantinos Daskalakis, Ioannis Panageas |
| 2018 | The Limits of Post-Selection Generalization. Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke |
| 2018 | The Lingering of Gradients: How to Reuse Gradients Over Time. Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang |
| 2018 | The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal. Jiantao Jiao, Weihao Gao, Yanjun Han |
| 2018 | The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. Jesse H. Krijthe, Marco Loog |
| 2018 | The Physical Systems Behind Optimization Algorithms. Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao |
| 2018 | The Price of Fair PCA: One Extra dimension. Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala |
| 2018 | The Price of Privacy for Low-rank Factorization. Jalaj Upadhyay |
| 2018 | The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing |
| 2018 | The Sparse Manifold Transform. Yubei Chen, Dylan M. Paiton, Bruno A. Olshausen |
| 2018 | The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network. Jeffrey Pennington, Pratik Worah |
| 2018 | The challenge of realistic music generation: modelling raw audio at scale. Sander Dieleman, Aäron van den Oord, Karen Simonyan |
| 2018 | The committee machine: Computational to statistical gaps in learning a two-layers neural network. Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová |
| 2018 | The emergence of multiple retinal cell types through efficient coding of natural movies. Samuel A. Ocko, Jack Lindsey, Surya Ganguli, Stéphane Deny |
| 2018 | The promises and pitfalls of Stochastic Gradient Langevin Dynamics. Nicolas Brosse, Alain Durmus, Eric Moulines |
| 2018 | The streaming rollout of deep networks - towards fully model-parallel execution. Volker Fischer, Jan Köhler, Thomas Pfeil |
| 2018 | Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds. Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin |
| 2018 | Theoretical guarantees for EM under misspecified Gaussian mixture models. Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan |
| 2018 | Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning. Rui Luo, Jianhong Wang, Yaodong Yang, Jun Wang, Zhanxing Zhu |
| 2018 | Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. Yaodong Yu, Pan Xu, Quanquan Gu |
| 2018 | Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform. Mitali Bafna, Jack Murtagh, Nikhil Vyas |
| 2018 | Tight Bounds for Collaborative PAC Learning via Multiplicative Weights. Jiecao Chen, Qin Zhang, Yuan Zhou |
| 2018 | To Trust Or Not To Trust A Classifier. Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta |
| 2018 | Toddler-Inspired Visual Object Learning. Sven Bambach, David J. Crandall, Linda B. Smith, Chen Yu |
| 2018 | TopRank: A practical algorithm for online stochastic ranking. Tor Lattimore, Branislav Kveton, Shuai Li, Csaba Szepesvári |
| 2018 | Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements. Ankush Mandal, He Jiang, Anshumali Shrivastava, Vivek Sarkar |
| 2018 | Total stochastic gradient algorithms and applications in reinforcement learning. Paavo Parmas |
| 2018 | Towards Deep Conversational Recommendations. Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal |
| 2018 | Towards Robust Detection of Adversarial Examples. Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu |
| 2018 | Towards Robust Interpretability with Self-Explaining Neural Networks. David Alvarez-Melis, Tommi S. Jaakkola |
| 2018 | Towards Text Generation with Adversarially Learned Neural Outlines. Sandeep Subramanian, Sai Rajeswar, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, Chris Pal |
| 2018 | Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization. Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao |
| 2018 | Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John E. Hopcroft |
| 2018 | Trading robust representations for sample complexity through self-supervised visual experience. Andrea Tacchetti, Stephen Voinea, Georgios Evangelopoulos |
| 2018 | Training DNNs with Hybrid Block Floating Point. Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi |
| 2018 | Training Deep Models Faster with Robust, Approximate Importance Sampling. Tyler B. Johnson, Carlos Guestrin |
| 2018 | Training Deep Neural Networks with 8-bit Floating Point Numbers. Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan |
| 2018 | Training Neural Networks Using Features Replay. Zhouyuan Huo, Bin Gu, Heng Huang |
| 2018 | Training deep learning based denoisers without ground truth data. Shakarim Soltanayev, Se Young Chun |
| 2018 | Trajectory Convolution for Action Recognition. Yue Zhao, Yuanjun Xiong, Dahua Lin |
| 2018 | Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio López-Moreno, Yonghui Wu |
| 2018 | Transfer Learning with Neural AutoML. Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo |
| 2018 | Transfer of Deep Reactive Policies for MDP Planning. Aniket (Nick) Bajpai, Sankalp Garg, Mausam |
| 2018 | Transfer of Value Functions via Variational Methods. Andrea Tirinzoni, Rafael Rodríguez-Sánchez, Marcello Restelli |
| 2018 | Tree-to-tree Neural Networks for Program Translation. Xinyun Chen, Chang Liu, Dawn Song |
| 2018 | Turbo Learning for CaptionBot and DrawingBot. Qiuyuan Huang, Pengchuan Zhang, Dapeng Oliver Wu, Lei Zhang |
| 2018 | Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. Stephen Mussmann, Percy Liang |
| 2018 | Uncertainty-Aware Attention for Reliable Interpretation and Prediction. Jay Heo, Haebeom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang |
| 2018 | Understanding Batch Normalization. Johan Bjorck, Carla P. Gomes, Bart Selman, Kilian Q. Weinberger |
| 2018 | Understanding Regularized Spectral Clustering via Graph Conductance. Yilin Zhang, Karl Rohe |
| 2018 | Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units. Yixi Xu, Xiao Wang |
| 2018 | Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners. Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue |
| 2018 | Uniform Convergence of Gradients for Non-Convex Learning and Optimization. Dylan J. Foster, Ayush Sekhari, Karthik Sridharan |
| 2018 | Universal Growth in Production Economies. Simina Brânzei, Ruta Mehta, Noam Nisan |
| 2018 | Unorganized Malicious Attacks Detection. Ming Pang, Wei Gao, Min Tao, Zhi-Hua Zhou |
| 2018 | Unsupervised Adversarial Invariance. Ayush Jaiswal, Rex Yue Wu, Wael Abd-Almageed, Prem Natarajan |
| 2018 | Unsupervised Attention-guided Image-to-Image Translation. Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim |
| 2018 | Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces. Yu-An Chung, Wei-Hung Weng, Schrasing Tong, James R. Glass |
| 2018 | Unsupervised Depth Estimation, 3D Face Rotation and Replacement. Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Chris Pal |
| 2018 | Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound. Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi |
| 2018 | Unsupervised Learning of Artistic Styles with Archetypal Style Analysis. Daan Wynen, Cordelia Schmid, Julien Mairal |
| 2018 | Unsupervised Learning of Object Landmarks through Conditional Image Generation. Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi |
| 2018 | Unsupervised Learning of Shape and Pose with Differentiable Point Clouds. Eldar Insafutdinov, Alexey Dosovitskiy |
| 2018 | Unsupervised Learning of View-invariant Action Representations. Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli |
| 2018 | Unsupervised Text Style Transfer using Language Models as Discriminators. Zichao Yang, Zhiting Hu, Chris Dyer, Eric P. Xing, Taylor Berg-Kirkpatrick |
| 2018 | Unsupervised Video Object Segmentation for Deep Reinforcement Learning. Vikash Goel, Jameson Weng, Pascal Poupart |
| 2018 | Uplift Modeling from Separate Labels. Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama |
| 2018 | Using Large Ensembles of Control Variates for Variational Inference. Tomas Geffner, Justin Domke |
| 2018 | Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel |
| 2018 | Variance-Reduced Stochastic Gradient Descent on Streaming Data. Ellango Jothimurugesan, Ashraf Tahmasbi, Phillip B. Gibbons, Srikanta Tirthapura |
| 2018 | Variational Bayesian Monte Carlo. Luigi Acerbi |
| 2018 | Variational Inference with Tail-adaptive f-Divergence. Dilin Wang, Hao Liu, Qiang Liu |
| 2018 | Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition. Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine |
| 2018 | Variational Learning on Aggregate Outputs with Gaussian Processes. Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu |
| 2018 | Variational Memory Encoder-Decoder. Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh |
| 2018 | Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms. Ganesh Sundaramoorthi, Anthony J. Yezzi |
| 2018 | Verifiable Reinforcement Learning via Policy Extraction. Osbert Bastani, Yewen Pu, Armando Solar-Lezama |
| 2018 | Video Prediction via Selective Sampling. Jingwei Xu, Bingbing Ni, Xiaokang Yang |
| 2018 | Video-to-Video Synthesis. Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Nikolai Yakovenko, Andrew Tao, Jan Kautz, Bryan Catanzaro |
| 2018 | VideoCapsuleNet: A Simplified Network for Action Detection. Kevin Duarte, Yogesh S. Rawat, Mubarak Shah |
| 2018 | Virtual Class Enhanced Discriminative Embedding Learning. Binghui Chen, Weihong Deng, Haifeng Shen |
| 2018 | Visual Memory for Robust Path Following. Ashish Kumar, Saurabh Gupta, David F. Fouhey, Sergey Levine, Jitendra Malik |
| 2018 | Visual Object Networks: Image Generation with Disentangled 3D Representations. Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, Bill Freeman |
| 2018 | Visual Reinforcement Learning with Imagined Goals. Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine |
| 2018 | Visualizing the Loss Landscape of Neural Nets. Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein |
| 2018 | Wasserstein Distributionally Robust Kalman Filtering. Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani |
| 2018 | Wasserstein Variational Inference. Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, Eric Maris |
| 2018 | Watch Your Step: Learning Node Embeddings via Graph Attention. Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi |
| 2018 | Wavelet regression and additive models for irregularly spaced data. Asad Haris, Ali Shojaie, Noah Simon |
| 2018 | Weakly Supervised Dense Event Captioning in Videos. Xuguang Duan, Wen-bing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang |
| 2018 | When do random forests fail? Cheng Tang, Damien Garreau, Ulrike von Luxburg |
| 2018 | Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior. Siddharth Reddy, Anca D. Dragan, Sergey Levine |
| 2018 | Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients? Boris Hanin |
| 2018 | Why Is My Classifier Discriminatory? Irene Y. Chen, Fredrik D. Johansson, David A. Sontag |
| 2018 | Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task. Dalin Guo, Angela J. Yu |
| 2018 | With Friends Like These, Who Needs Adversaries? Saumya Jetley, Nicholas A. Lord, Philip H. S. Torr |
| 2018 | Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning. Ofir Marom, Benjamin Rosman |
| 2018 | Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization. Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, Lisa Amini |
| 2018 | Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates. Krishnakumar Balasubramanian, Saeed Ghadimi |
| 2018 | \ell_1-regression with Heavy-tailed Distributions. Lijun Zhang, Zhi-Hua Zhou |
| 2018 | cpSGD: Communication-efficient and differentially-private distributed SGD. Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan |
| 2018 | e-SNLI: Natural Language Inference with Natural Language Explanations. Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, Phil Blunsom |
| 2018 | rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions. Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Steeve Dibangoye |