| 2018 | A Boo(n) for Evaluating Architecture Performance. Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst |
| 2018 | A Classification-Based Study of Covariate Shift in GAN Distributions. Shibani Santurkar, Ludwig Schmidt, Aleksander Madry |
| 2018 | A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming. Alp Yurtsever, Olivier Fercoq, Francesco Locatello, Volkan Cevher |
| 2018 | A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning. Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini |
| 2018 | A Distributed Second-Order Algorithm You Can Trust. Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi |
| 2018 | A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models. Beilun Wang, Arshdeep Sekhon, Yanjun Qi |
| 2018 | A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music. Adam Roberts, Jesse H. Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck |
| 2018 | A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu |
| 2018 | A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization. Robin Vogel, Aurélien Bellet, Stéphan Clémençon |
| 2018 | A Progressive Batching L-BFGS Method for Machine Learning. Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang |
| 2018 | A Reductions Approach to Fair Classification. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach |
| 2018 | A Robust Approach to Sequential Information Theoretic Planning. Sue Zheng, Jason Pacheco, John W. Fisher III |
| 2018 | A Semantic Loss Function for Deep Learning with Symbolic Knowledge. Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Van den Broeck |
| 2018 | A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates. Kaiwen Zhou, Fanhua Shang, James Cheng |
| 2018 | A Spectral Approach to Gradient Estimation for Implicit Distributions. Jiaxin Shi, Shengyang Sun, Jun Zhu |
| 2018 | A Spline Theory of Deep Networks. Randall Balestriero, Richard G. Baraniuk |
| 2018 | A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations. Weili Nie, Yang Zhang, Ankit Patel |
| 2018 | A Two-Step Computation of the Exact GAN Wasserstein Distance. Huidong Liu, Xianfeng Gu, Dimitris Samaras |
| 2018 | A Unified Framework for Structured Low-rank Matrix Learning. Pratik Jawanpuria, Bamdev Mishra |
| 2018 | A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks. Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira |
| 2018 | ADMM and Accelerated ADMM as Continuous Dynamical Systems. Guilherme França, Daniel P. Robinson, René Vidal |
| 2018 | Accelerated Spectral Ranking. Arpit Agarwal, Prathamesh Patil, Shivani Agarwal |
| 2018 | Accelerating Greedy Coordinate Descent Methods. Haihao Lu, Robert M. Freund, Vahab S. Mirrokni |
| 2018 | Accelerating Natural Gradient with Higher-Order Invariance. Yang Song, Jiaming Song, Stefano Ermon |
| 2018 | Accurate Inference for Adaptive Linear Models. Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy |
| 2018 | Accurate Uncertainties for Deep Learning Using Calibrated Regression. Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon |
| 2018 | Active Learning with Logged Data. Songbai Yan, Kamalika Chaudhuri, Tara Javidi |
| 2018 | Active Testing: An Efficient and Robust Framework for Estimating Accuracy. Phuc Xuan Nguyen, Deva Ramanan, Charless C. Fowlkes |
| 2018 | Adafactor: Adaptive Learning Rates with Sublinear Memory Cost. Noam Shazeer, Mitchell Stern |
| 2018 | Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits. Huasen Wu, Xueying Guo, Xin Liu |
| 2018 | Adaptive Sampled Softmax with Kernel Based Sampling. Guy Blanc, Steffen Rendle |
| 2018 | Adaptive Three Operator Splitting. Fabian Pedregosa, Gauthier Gidel |
| 2018 | Addressing Function Approximation Error in Actor-Critic Methods. Scott Fujimoto, Herke van Hoof, David Meger |
| 2018 | Adversarial Attack on Graph Structured Data. Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song |
| 2018 | Adversarial Distillation of Bayesian Neural Network Posteriors. Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel |
| 2018 | Adversarial Learning with Local Coordinate Coding. Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan |
| 2018 | Adversarial Regression with Multiple Learners. Liang Tong, Sixie Yu, Scott Alfeld, Yevgeniy Vorobeychik |
| 2018 | Adversarial Risk and the Dangers of Evaluating Against Weak Attacks. Jonathan Uesato, Brendan O'Donoghue, Pushmeet Kohli, Aäron van den Oord |
| 2018 | Adversarial Time-to-Event Modeling. Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Alan Goldstein, Lawrence Carin, Ricardo Henao |
| 2018 | Adversarially Regularized Autoencoders. Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun |
| 2018 | Alternating Randomized Block Coordinate Descent. Jelena Diakonikolas, Lorenzo Orecchia |
| 2018 | An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method. Li Shen, Peng Sun, Yitong Wang, Wei Liu, Tong Zhang |
| 2018 | An Alternative View: When Does SGD Escape Local Minima? Robert Kleinberg, Yuanzhi Li, Yang Yuan |
| 2018 | An Efficient Semismooth Newton Based Algorithm for Convex Clustering. Yancheng Yuan, Defeng Sun, Kim-Chuan Toh |
| 2018 | An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning. Dhruv Malik, Malayandi Palaniappan, Jaime F. Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca D. Dragan |
| 2018 | An Estimation and Analysis Framework for the Rasch Model. Andrew S. Lan, Mung Chiang, Christoph Studer |
| 2018 | An Inference-Based Policy Gradient Method for Learning Options. Matthew J. A. Smith, Herke van Hoof, Joelle Pineau |
| 2018 | An Iterative, Sketching-based Framework for Ridge Regression. Agniva Chowdhury, Jiasen Yang, Petros Drineas |
| 2018 | An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. Qianxiao Li, Shuji Hao |
| 2018 | Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. Hideaki Imamura, Issei Sato, Masashi Sugiyama |
| 2018 | Analyzing Uncertainty in Neural Machine Translation. Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato |
| 2018 | Analyzing the Robustness of Nearest Neighbors to Adversarial Examples. Yizhen Wang, Somesh Jha, Kamalika Chaudhuri |
| 2018 | Anonymous Walk Embeddings. Sergey Ivanov, Evgeny Burnaev |
| 2018 | Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni |
| 2018 | Approximate message passing for amplitude based optimization. Junjie Ma, Ji Xu, Arian Maleki |
| 2018 | Approximation Algorithms for Cascading Prediction Models. Matthew Streeter |
| 2018 | Approximation Guarantees for Adaptive Sampling. Eric Balkanski, Yaron Singer |
| 2018 | Asynchronous Byzantine Machine Learning (the case of SGD). Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki |
| 2018 | Asynchronous Decentralized Parallel Stochastic Gradient Descent. Xiangru Lian, Wei Zhang, Ce Zhang, Ji Liu |
| 2018 | Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization. Umut Simsekli, Çagatay Yildiz, Thanh Huy Nguyen, A. Taylan Cemgil, Gaël Richard |
| 2018 | Attention-based Deep Multiple Instance Learning. Maximilian Ilse, Jakub M. Tomczak, Max Welling |
| 2018 | Augment and Reduce: Stochastic Inference for Large Categorical Distributions. Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei |
| 2018 | Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data. Amjad Almahairi, Sai Rajeswar, Alessandro Sordoni, Philip Bachman, Aaron C. Courville |
| 2018 | AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning. Ahmed M. Alaa, Mihaela van der Schaar |
| 2018 | Automatic Goal Generation for Reinforcement Learning Agents. Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel |
| 2018 | Autoregressive Convolutional Neural Networks for Asynchronous Time Series. Mikolaj Binkowski, Gautier Marti, Philippe Donnat |
| 2018 | Autoregressive Quantile Networks for Generative Modeling. Georg Ostrovski, Will Dabney, Rémi Munos |
| 2018 | BOCK : Bayesian Optimization with Cylindrical Kernels. ChangYong Oh, Efstratios Gavves, Max Welling |
| 2018 | BOHB: Robust and Efficient Hyperparameter Optimization at Scale. Stefan Falkner, Aaron Klein, Frank Hutter |
| 2018 | Bandits with Delayed, Aggregated Anonymous Feedback. Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvári, Steffen Grünewälder |
| 2018 | Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design. Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, Xuan Zeng |
| 2018 | Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. Trevor Campbell, Tamara Broderick |
| 2018 | Bayesian Model Selection for Change Point Detection and Clustering. Othmane Mazhar, Cristian R. Rojas, Carlo Fischione, Mohammad Reza Hesamzadeh |
| 2018 | Bayesian Optimization of Combinatorial Structures. Ricardo Baptista, Matthias Poloczek |
| 2018 | Bayesian Quadrature for Multiple Related Integrals. Xiaoyue Xi, François-Xavier Briol, Mark A. Girolami |
| 2018 | Bayesian Uncertainty Estimation for Batch Normalized Deep Networks. Mattias Teye, Hossein Azizpour, Kevin Smith |
| 2018 | Been There, Done That: Meta-Learning with Episodic Recall. Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick |
| 2018 | Best Arm Identification in Linear Bandits with Linear Dimension Dependency. Chao Tao, Saúl A. Blanco, Yuan Zhou |
| 2018 | Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams. Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson |
| 2018 | Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations. Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong |
| 2018 | Beyond the One-Step Greedy Approach in Reinforcement Learning. Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor |
| 2018 | Bilevel Programming for Hyperparameter Optimization and Meta-Learning. Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil |
| 2018 | Binary Classification with Karmic, Threshold-Quasi-Concave Metrics. Bowei Yan, Oluwasanmi Koyejo, Kai Zhong, Pradeep Ravikumar |
| 2018 | Binary Partitions with Approximate Minimum Impurity. Eduardo Sany Laber, Marco Molinaro, Felipe de A. Mello Pereira |
| 2018 | Black Box FDR. Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan |
| 2018 | Black-Box Variational Inference for Stochastic Differential Equations. Thomas Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle |
| 2018 | Black-box Adversarial Attacks with Limited Queries and Information. Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin |
| 2018 | Blind Justice: Fairness with Encrypted Sensitive Attributes. Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller |
| 2018 | Born-Again Neural Networks. Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar |
| 2018 | Bounding and Counting Linear Regions of Deep Neural Networks. Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam |
| 2018 | Bounds on the Approximation Power of Feedforward Neural Networks. Mohammad Mehrabi, Aslan Tchamkerten, Mansoor I. Yousefi |
| 2018 | Bucket Renormalization for Approximate Inference. Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin |
| 2018 | Budgeted Experiment Design for Causal Structure Learning. AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim |
| 2018 | Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett |
| 2018 | CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning. Wissam Siblini, Frank Meyer, Pascale Kuntz |
| 2018 | CRVI: Convex Relaxation for Variational Inference. Ghazal Fazelnia, John W. Paisley |
| 2018 | Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg |
| 2018 | Candidates vs. Noises Estimation for Large Multi-Class Classification Problem. Lei Han, Yiheng Huang, Tong Zhang |
| 2018 | Canonical Tensor Decomposition for Knowledge Base Completion. Timothée Lacroix, Nicolas Usunier, Guillaume Obozinski |
| 2018 | Causal Bandits with Propagating Inference. Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi |
| 2018 | Celer: a Fast Solver for the Lasso with Dual Extrapolation. Mathurin Massias, Joseph Salmon, Alexandre Gramfort |
| 2018 | Characterizing Implicit Bias in Terms of Optimization Geometry. Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro |
| 2018 | Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions. Karren D. Yang, Abigail Katoff, Caroline Uhler |
| 2018 | Chi-square Generative Adversarial Network. Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin |
| 2018 | Classification from Pairwise Similarity and Unlabeled Data. Han Bao, Gang Niu, Masashi Sugiyama |
| 2018 | Clipped Action Policy Gradient. Yasuhiro Fujita, Shin-ichi Maeda |
| 2018 | Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization. Louis Filstroff, Alberto Lumbreras, Cédric Févotte |
| 2018 | Clustering Semi-Random Mixtures of Gaussians. Pranjal Awasthi, Aravindan Vijayaraghavan |
| 2018 | CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions. Kevin Tian, Teng Zhang, James Zou |
| 2018 | Coded Sparse Matrix Multiplication. Sinong Wang, Jiashang Liu, Ness B. Shroff |
| 2018 | Communication-Computation Efficient Gradient Coding. Min Ye, Emmanuel Abbe |
| 2018 | Comparing Dynamics: Deep Neural Networks versus Glassy Systems. Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli |
| 2018 | Comparison-Based Random Forests. Siavash Haghiri, Damien Garreau, Ulrike von Luxburg |
| 2018 | Competitive Caching with Machine Learned Advice. Thodoris Lykouris, Sergei Vassilvitskii |
| 2018 | Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations. Xingyu Wang, Diego Klabjan |
| 2018 | Compiling Combinatorial Prediction Games. Frédéric Koriche |
| 2018 | Composable Planning with Attributes. Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus |
| 2018 | Composite Functional Gradient Learning of Generative Adversarial Models. Rie Johnson, Tong Zhang |
| 2018 | Composite Marginal Likelihood Methods for Random Utility Models. Zhibing Zhao, Lirong Xia |
| 2018 | Compressing Neural Networks using the Variational Information Bottleneck. Bin Dai, Chen Zhu, Baining Guo, David P. Wipf |
| 2018 | Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm. Pavel E. Dvurechensky, Alexander V. Gasnikov, Alexey Kroshnin |
| 2018 | Conditional Neural Processes. Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami |
| 2018 | Conditional Noise-Contrastive Estimation of Unnormalised Models. Ciwan Ceylan, Michael U. Gutmann |
| 2018 | Configurable Markov Decision Processes. Alberto Maria Metelli, Mirco Mutti, Marcello Restelli |
| 2018 | Constant-Time Predictive Distributions for Gaussian Processes. Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson |
| 2018 | Constrained Interacting Submodular Groupings. Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya R. Gupta, Jeff A. Bilmes |
| 2018 | Constraining the Dynamics of Deep Probabilistic Models. Marco Lorenzi, Maurizio Filippone |
| 2018 | ContextNet: Deep learning for Star Galaxy Classification. Noble Kennamer, David Kirkby, Alexander Ihler, Francisco Javier Sanchez-Lopez |
| 2018 | Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. Davide Bacciu, Federico Errica, Alessio Micheli |
| 2018 | Continual Reinforcement Learning with Complex Synapses. Christos Kaplanis, Murray Shanahan, Claudia Clopath |
| 2018 | Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. Pan Xu, Tianhao Wang, Quanquan Gu |
| 2018 | Continuous-Time Flows for Efficient Inference and Density Estimation. Changyou Chen, Chunyuan Li, Liquan Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin |
| 2018 | Convergence guarantees for a class of non-convex and non-smooth optimization problems. Koulik Khamaru, Martin J. Wainwright |
| 2018 | Convergent TREE BACKUP and RETRACE with Function Approximation. Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent |
| 2018 | Convolutional Imputation of Matrix Networks. Qingyun Sun, Mengyuan Yan, David L. Donoho, Stephen P. Boyd |
| 2018 | Coordinated Exploration in Concurrent Reinforcement Learning. Maria Dimakopoulou, Benjamin Van Roy |
| 2018 | Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu |
| 2018 | Crowdsourcing with Arbitrary Adversaries. Matthäus Kleindessner, Pranjal Awasthi |
| 2018 | Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks. Daphna Weinshall, Gad Cohen, Dan Amir |
| 2018 | Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation. Hugo Raguet, Loïc Landrieu |
| 2018 | CyCADA: Cycle-Consistent Adversarial Domain Adaptation. Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell |
| 2018 | D Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu |
| 2018 | DCFNet: Deep Neural Network with Decomposed Convolutional Filters. Qiang Qiu, Xiuyuan Cheng, A. Robert Calderbank, Guillermo Sapiro |
| 2018 | DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding. Thomas Moreau, Laurent Oudre, Nicolas Vayatis |
| 2018 | DRACO: Byzantine-resilient Distributed Training via Redundant Gradients. Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris S. Papailiopoulos |
| 2018 | DVAE++: Discrete Variational Autoencoders with Overlapping Transformations. Arash Vahdat, William G. Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash |
| 2018 | Data Summarization at Scale: A Two-Stage Submodular Approach. Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi |
| 2018 | Data-Dependent Stability of Stochastic Gradient Descent. Ilja Kuzborskij, Christoph H. Lampert |
| 2018 | Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings. Aryan Mokhtari, Hamed Hassani, Amin Karbasi |
| 2018 | Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning. Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft |
| 2018 | Decoupled Parallel Backpropagation with Convergence Guarantee. Zhouyuan Huo, Bin Gu, Qian Yang, Heng Huang |
| 2018 | Decoupling Gradient-Like Learning Rules from Representations. Philip S. Thomas, Christoph Dann, Emma Brunskill |
| 2018 | Deep Asymmetric Multi-task Feature Learning. Haebeom Lee, Eunho Yang, Sung Ju Hwang |
| 2018 | Deep Bayesian Nonparametric Tracking. Aonan Zhang, John W. Paisley |
| 2018 | Deep Density Destructors. David I. Inouye, Pradeep Ravikumar |
| 2018 | Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global. Thomas Laurent, James von Brecht |
| 2018 | Deep Models of Interactions Across Sets. Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh |
| 2018 | Deep One-Class Classification. Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert A. Vandermeulen, Alexander Binder, Emmanuel Müller, Marius Kloft |
| 2018 | Deep Predictive Coding Network for Object Recognition. Haiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, Zhongming Liu |
| 2018 | Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling. Kyowoon Lee, Sol-A. Kim, Jaesik Choi, Seong-Whan Lee |
| 2018 | Deep Variational Reinforcement Learning for POMDPs. Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson |
| 2018 | Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions. Junru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan, Yingyan Lin |
| 2018 | Delayed Impact of Fair Machine Learning. Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt |
| 2018 | Dependent Relational Gamma Process Models for Longitudinal Networks. Sikun Yang, Heinz Koeppl |
| 2018 | Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches. Simon Olofsson, Marc Peter Deisenroth, Ruth Misener |
| 2018 | Detecting and Correcting for Label Shift with Black Box Predictors. Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola |
| 2018 | Detecting non-causal artifacts in multivariate linear regression models. Dominik Janzing, Bernhard Schölkopf |
| 2018 | DiCE: The Infinitely Differentiable Monte Carlo Estimator. Jakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric P. Xing, Shimon Whiteson |
| 2018 | Differentiable Abstract Interpretation for Provably Robust Neural Networks. Matthew Mirman, Timon Gehr, Martin T. Vechev |
| 2018 | Differentiable Compositional Kernel Learning for Gaussian Processes. Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse |
| 2018 | Differentiable Dynamic Programming for Structured Prediction and Attention. Arthur Mensch, Mathieu Blondel |
| 2018 | Differentiable plasticity: training plastic neural networks with backpropagation. Thomas Miconi, Kenneth O. Stanley, Jeff Clune |
| 2018 | Differentially Private Database Release via Kernel Mean Embeddings. Matej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf |
| 2018 | Differentially Private Identity and Equivalence Testing of Discrete Distributions. Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld |
| 2018 | Differentially Private Matrix Completion Revisited. Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta |
| 2018 | Dimensionality-Driven Learning with Noisy Labels. Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi N. R. Wijewickrema, James Bailey |
| 2018 | Discovering Interpretable Representations for Both Deep Generative and Discriminative Models. Tameem Adel, Zoubin Ghahramani, Adrian Weller |
| 2018 | Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning. Thomas G. Dietterich, George Trimponias, Zhitang Chen |
| 2018 | Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms. Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon S. Du, Stuart Russell |
| 2018 | Disentangled Sequential Autoencoder. Yingzhen Li, Stephan Mandt |
| 2018 | Disentangling by Factorising. Hyunjik Kim, Andriy Mnih |
| 2018 | Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. Lukas Balles, Philipp Hennig |
| 2018 | Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs. Bin Hu, Stephen J. Wright, Laurent Lessard |
| 2018 | Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go? Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei |
| 2018 | Distributed Clustering via LSH Based Data Partitioning. Aditya Bhaskara, Maheshakya Wijewardena |
| 2018 | Distributed Nonparametric Regression under Communication Constraints. Yuancheng Zhu, John Lafferty |
| 2018 | Do Outliers Ruin Collaboration? Mingda Qiao |
| 2018 | Does Distributionally Robust Supervised Learning Give Robust Classifiers? Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama |
| 2018 | Dropout Training, Data-dependent Regularization, and Generalization Bounds. Wenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang |
| 2018 | Dynamic Evaluation of Neural Sequence Models. Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals |
| 2018 | Dynamic Regret of Strongly Adaptive Methods. Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou |
| 2018 | Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington |
| 2018 | Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks. Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz |
| 2018 | Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner |
| 2018 | Efficient First-Order Algorithms for Adaptive Signal Denoising. Dmitrii Ostrovskii, Zaïd Harchaoui |
| 2018 | Efficient Gradient-Free Variational Inference using Policy Search. Oleg Arenz, Mingjun Zhong, Gerhard Neumann |
| 2018 | Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation. Dane S. Corneil, Wulfram Gerstner, Johanni Brea |
| 2018 | Efficient Neural Architecture Search via Parameter Sharing. Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean |
| 2018 | Efficient Neural Audio Synthesis. Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, Koray Kavukcuoglu |
| 2018 | Efficient and Consistent Adversarial Bipartite Matching. Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian D. Ziebart |
| 2018 | Efficient end-to-end learning for quantizable representations. Yeonwoo Jeong, Hyun Oh Song |
| 2018 | End-to-End Learning for the Deep Multivariate Probit Model. Di Chen, Yexiang Xue, Carla P. Gomes |
| 2018 | End-to-end Active Object Tracking via Reinforcement Learning. Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang |
| 2018 | Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors. Gintare Karolina Dziugaite, Daniel M. Roy |
| 2018 | Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory. Guillaume Pouliot |
| 2018 | Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization. Jiaxiang Wu, Weidong Huang, Junzhou Huang, Tong Zhang |
| 2018 | Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. Miles E. Lopes, Shusen Wang, Michael W. Mahoney |
| 2018 | Escaping Saddles with Stochastic Gradients. Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann |
| 2018 | Essentially No Barriers in Neural Network Energy Landscape. Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht |
| 2018 | Estimation of Markov Chain via Rank-constrained Likelihood. Xudong Li, Mengdi Wang, Anru Zhang |
| 2018 | Explicit Inductive Bias for Transfer Learning with Convolutional Networks. Xuhong Li, Yves Grandvalet, Franck Davoine |
| 2018 | Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search. Masanori Suganuma, Mete Ozay, Takayuki Okatani |
| 2018 | Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks. Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken |
| 2018 | Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples. Gail Weiss, Yoav Goldberg, Eran Yahav |
| 2018 | Extreme Learning to Rank via Low Rank Assumption. Minhao Cheng, Ian Davidson, Cho-Jui Hsieh |
| 2018 | Fair and Diverse DPP-Based Data Summarization. L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth K. Vishnoi |
| 2018 | Fairness Without Demographics in Repeated Loss Minimization. Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang |
| 2018 | Fast Approximate Spectral Clustering for Dynamic Networks. Lionel Martin, Andreas Loukas, Pierre Vandergheynst |
| 2018 | Fast Bellman Updates for Robust MDPs. Chin Pang Ho, Marek Petrik, Wolfram Wiesemann |
| 2018 | Fast Decoding in Sequence Models Using Discrete Latent Variables. Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, Noam Shazeer |
| 2018 | Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework. Arman Sharifi Kolarijani, Peyman Mohajerin Esfahani, Tamás Keviczky |
| 2018 | Fast Information-theoretic Bayesian Optimisation. Bin Xin Ru, Mark McLeod, Diego Granziol, Michael A. Osborne |
| 2018 | Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice. Alan Kuhnle, J. David Smith, Victoria G. Crawford, My T. Thai |
| 2018 | Fast Parametric Learning with Activation Memorization. Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap |
| 2018 | Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate. Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang |
| 2018 | Fast Variance Reduction Method with Stochastic Batch Size. Xuanqing Liu, Cho-Jui Hsieh |
| 2018 | Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. Xiao Zhang, Simon S. Du, Quanquan Gu |
| 2018 | Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava |
| 2018 | Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines. Bin Gu, Zhouyuan Huo, Cheng Deng, Heng Huang |
| 2018 | Feasible Arm Identification. Julian Katz-Samuels, Clayton Scott |
| 2018 | Feedback-Based Tree Search for Reinforcement Learning. Daniel R. Jiang, Emmanuel Ekwedike, Han Liu |
| 2018 | Finding Influential Training Samples for Gradient Boosted Decision Trees. Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, Maarten de Rijke |
| 2018 | Firing Bandits: Optimizing Crowdfunding. Lalit Jain, Kevin Jamieson |
| 2018 | First Order Generative Adversarial Networks. Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter |
| 2018 | Fitting New Speakers Based on a Short Untranscribed Sample. Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf |
| 2018 | Fixing a Broken ELBO. Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy |
| 2018 | Focused Hierarchical RNNs for Conditional Sequence Processing. Nan Rosemary Ke, Konrad Zolna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Christopher J. Pal |
| 2018 | Fourier Policy Gradients. Matthew Fellows, Kamil Ciosek, Shimon Whiteson |
| 2018 | Frank-Wolfe with Subsampling Oracle. Thomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont |
| 2018 | Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar |
| 2018 | Functional Gradient Boosting based on Residual Network Perception. Atsushi Nitanda, Taiji Suzuki |
| 2018 | GAIN: Missing Data Imputation using Generative Adversarial Nets. Jinsung Yoon, James Jordon, Mihaela van der Schaar |
| 2018 | GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms. Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer |
| 2018 | Gated Path Planning Networks. Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric P. Xing, Ruslan Salakhutdinov |
| 2018 | Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. Brenden M. Lake, Marco Baroni |
| 2018 | Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction. Siyuan Qi, Baoxiong Jia, Song-Chun Zhu |
| 2018 | Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression. Haitao Liu, Jianfei Cai, Yi Wang, Yew-Soon Ong |
| 2018 | Generative Temporal Models with Spatial Memory for Partially Observed Environments. Marco Fraccaro, Danilo Jimenez Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola |
| 2018 | Geodesic Convolutional Shape Optimization. Pierre Baqué, Edoardo Remelli, François Fleuret, Pascal Fua |
| 2018 | Geometry Score: A Method For Comparing Generative Adversarial Networks. Valentin Khrulkov, Ivan V. Oseledets |
| 2018 | Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator. Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi |
| 2018 | Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy. Jiasen Yang, Qiang Liu, Vinayak A. Rao, Jennifer Neville |
| 2018 | GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich |
| 2018 | Gradient Coding from Cyclic MDS Codes and Expander Graphs. Netanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo |
| 2018 | Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos |
| 2018 | Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers. Yao Ma, Alexander Olshevsky, Csaba Szepesvári, Venkatesh Saligrama |
| 2018 | Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks. Mingyi Hong, Meisam Razaviyayn, Jason D. Lee |
| 2018 | Gradient descent with identity initialization efficiently learns positive definite linear transformations. Peter L. Bartlett, David P. Helmbold, Philip M. Long |
| 2018 | Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace. Yoonho Lee, Seungjin Choi |
| 2018 | Gradually Updated Neural Networks for Large-Scale Image Recognition. Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo Wang, Alan L. Yuille |
| 2018 | Graph Networks as Learnable Physics Engines for Inference and Control. Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin A. Riedmiller, Raia Hadsell, Peter W. Battaglia |
| 2018 | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models. Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec |
| 2018 | Graphical Nonconvex Optimization via an Adaptive Convex Relaxation. Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang |
| 2018 | Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions. Wenruo Bai, Jeffrey A. Bilmes |
| 2018 | Hierarchical Clustering with Structural Constraints. Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar |
| 2018 | Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series. Zhengping Che, Sanjay Purushotham, Max Guangyu Li, Bo Jiang, Yan Liu |
| 2018 | Hierarchical Imitation and Reinforcement Learning. Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III |
| 2018 | Hierarchical Long-term Video Prediction without Supervision. Nevan Wichers, Ruben Villegas, Dumitru Erhan, Honglak Lee |
| 2018 | Hierarchical Multi-Label Classification Networks. Jonatas Wehrmann, Ricardo Cerri, Rodrigo C. Barros |
| 2018 | Hierarchical Text Generation and Planning for Strategic Dialogue. Denis Yarats, Mike Lewis |
| 2018 | High Performance Zero-Memory Overhead Direct Convolutions. Jiyuan Zhang, Franz Franchetti, Tze Meng Low |
| 2018 | High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach. Tim Pearce, Alexandra Brintrup, Mohamed Zaki, Andy Neely |
| 2018 | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings. Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann |
| 2018 | IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu |
| 2018 | INSPECTRE: Privately Estimating the Unseen. Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang |
| 2018 | Image Transformer. Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran |
| 2018 | Implicit Quantile Networks for Distributional Reinforcement Learning. Will Dabney, Georg Ostrovski, David Silver, Rémi Munos |
| 2018 | Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion. Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen |
| 2018 | Importance Weighted Transfer of Samples in Reinforcement Learning. Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli |
| 2018 | Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. Daniele Calandriello, Ioannis Koutis, Alessandro Lazaric, Michal Valko |
| 2018 | Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems. Marc Abeille, Alessandro Lazaric |
| 2018 | Improved Training of Generative Adversarial Networks using Representative Features. Duhyeon Bang, Hyunjung Shim |
| 2018 | Improved nearest neighbor search using auxiliary information and priority functions. Omid Keivani, Kaushik Sinha |
| 2018 | Improving Optimization in Models With Continuous Symmetry Breaking. Robert Bamler, Stephan Mandt |
| 2018 | Improving Regression Performance with Distributional Losses. Ehsan Imani, Martha White |
| 2018 | Improving Sign Random Projections With Additional Information. Keegan Kang, Wong Wei Pin |
| 2018 | Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. Borja Balle, Yu-Xiang Wang |
| 2018 | Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms. Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu |
| 2018 | Inductive Two-layer Modeling with Parametric Bregman Transfer. Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu |
| 2018 | Inference Suboptimality in Variational Autoencoders. Chris Cremer, Xuechen Li, David Duvenaud |
| 2018 | Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization. Ibrahim M. Alabdulmohsin |
| 2018 | Inter and Intra Topic Structure Learning with Word Embeddings. He Zhao, Lan Du, Wray L. Buntine, Mingyuan Zhou |
| 2018 | Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres |
| 2018 | Invariance of Weight Distributions in Rectified MLPs. Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher |
| 2018 | Investigating Human Priors for Playing Video Games. Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei A. Efros |
| 2018 | Is Generator Conditioning Causally Related to GAN Performance? Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian J. Goodfellow |
| 2018 | Iterative Amortized Inference. Joseph Marino, Yisong Yue, Stephan Mandt |
| 2018 | JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets. Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin |
| 2018 | Junction Tree Variational Autoencoder for Molecular Graph Generation. Wengong Jin, Regina Barzilay, Tommi S. Jaakkola |
| 2018 | K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning. Jihun Hamm, Yung-Kyun Noh |
| 2018 | K-means clustering using random matrix sparsification. Kaushik Sinha |
| 2018 | Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization. Zeyuan Allen-Zhu |
| 2018 | Kernel Recursive ABC: Point Estimation with Intractable Likelihood. Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu |
| 2018 | Kernelized Synaptic Weight Matrices. Lorenz K. Müller, Julien N. P. Martel, Giacomo Indiveri |
| 2018 | Knowledge Transfer with Jacobian Matching. Suraj Srinivas, François Fleuret |
| 2018 | Kronecker Recurrent Units. Cijo Jose, Moustapha Cissé, François Fleuret |
| 2018 | LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration. Gellért Weisz, András György, Csaba Szepesvári |
| 2018 | LaVAN: Localized and Visible Adversarial Noise. Danny Karmon, Daniel Zoran, Yoav Goldberg |
| 2018 | Large-Scale Cox Process Inference using Variational Fourier Features. S. T. John, James Hensman |
| 2018 | Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion. Richard Y. Zhang, Salar Fattahi, Somayeh Sojoudi |
| 2018 | Latent Space Policies for Hierarchical Reinforcement Learning. Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine |
| 2018 | Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations. Ashwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra |
| 2018 | Learning Adversarially Fair and Transferable Representations. David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel |
| 2018 | Learning Binary Latent Variable Models: A Tensor Eigenpair Approach. Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler |
| 2018 | Learning Compact Neural Networks with Regularization. Samet Oymak |
| 2018 | Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry. Maximilian Nickel, Douwe Kiela |
| 2018 | Learning Deep ResNet Blocks Sequentially using Boosting Theory. Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire |
| 2018 | Learning Diffusion using Hyperparameters. Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg |
| 2018 | Learning Dynamics of Linear Denoising Autoencoders. Arnu Pretorius, Steve Kroon, Herman Kamper |
| 2018 | Learning Equations for Extrapolation and Control. Subham S. Sahoo, Christoph H. Lampert, Georg Martius |
| 2018 | Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling. Kejun Huang, Xiao Fu, Nicholas D. Sidiropoulos |
| 2018 | Learning Implicit Generative Models with the Method of Learned Moments. Suman V. Ravuri, Shakir Mohamed, Mihaela Rosca, Oriol Vinyals |
| 2018 | Learning Independent Causal Mechanisms. Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf |
| 2018 | Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations. Ting Chen, Martin Renqiang Min, Yizhou Sun |
| 2018 | Learning Localized Spatio-Temporal Models From Streaming Data. Muhammad Osama, Dave Zachariah, Thomas B. Schön |
| 2018 | Learning Long Term Dependencies via Fourier Recurrent Units. Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon |
| 2018 | Learning Longer-term Dependencies in RNNs with Auxiliary Losses. Trieu H. Trinh, Andrew M. Dai, Thang Luong, Quoc V. Le |
| 2018 | Learning Low-Dimensional Temporal Representations. Bing Su, Ying Wu |
| 2018 | Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time. Asish Ghoshal, Jean Honorio |
| 2018 | Learning Memory Access Patterns. Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan |
| 2018 | Learning One Convolutional Layer with Overlapping Patches. Surbhi Goel, Adam R. Klivans, Raghu Meka |
| 2018 | Learning Policy Representations in Multiagent Systems. Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yuri Burda, Harrison Edwards |
| 2018 | Learning Registered Point Processes from Idiosyncratic Observations. Hongteng Xu, Lawrence Carin, Hongyuan Zha |
| 2018 | Learning Representations and Generative Models for 3D Point Clouds. Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas |
| 2018 | Learning Semantic Representations for Unsupervised Domain Adaptation. Shaoan Xie, Zibin Zheng, Liang Chen, Chuan Chen |
| 2018 | Learning Steady-States of Iterative Algorithms over Graphs. Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song |
| 2018 | Learning a Mixture of Two Multinomial Logits. Flavio Chierichetti, Ravi Kumar, Andrew Tomkins |
| 2018 | Learning and Memorization. Satrajit Chatterjee |
| 2018 | Learning by Playing Solving Sparse Reward Tasks from Scratch. Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg |
| 2018 | Learning in Integer Latent Variable Models with Nested Automatic Differentiation. Daniel Sheldon, Kevin Winner, Debora Sujono |
| 2018 | Learning in Reproducing Kernel Krein Spaces. Dino Oglic, Thomas Gärtner |
| 2018 | Learning the Reward Function for a Misspecified Model. Erik Talvitie |
| 2018 | Learning to Act in Decentralized Partially Observable MDPs. Jilles Steeve Dibangoye, Olivier Buffet |
| 2018 | Learning to Branch. Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik |
| 2018 | Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems. Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé, Hado van Hasselt |
| 2018 | Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan |
| 2018 | Learning to Explore via Meta-Policy Gradient. Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng |
| 2018 | Learning to Optimize Combinatorial Functions. Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer |
| 2018 | Learning to Reweight Examples for Robust Deep Learning. Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun |
| 2018 | Learning to Search with MCTSnets. Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver |
| 2018 | Learning to Speed Up Structured Output Prediction. Xingyuan Pan, Vivek Srikumar |
| 2018 | Learning unknown ODE models with Gaussian processes. Markus Heinonen, Çagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki |
| 2018 | Learning with Abandonment. Sven Schmit, Ramesh Johari |
| 2018 | Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. Stephen Tu, Benjamin Recht |
| 2018 | Let's be Honest: An Optimal No-Regret Framework for Zero-Sum Games. Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher |
| 2018 | Level-Set Methods for Finite-Sum Constrained Convex Optimization. Qihang Lin, Runchao Ma, Tianbao Yang |
| 2018 | Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms. Graham Cormode, Charlie Dickens, David P. Woodruff |
| 2018 | Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data. Shuai Zheng, James Tin-Yau Kwok |
| 2018 | Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design. Ahmed M. Alaa, Mihaela van der Schaar |
| 2018 | Linear Spectral Estimators and an Application to Phase Retrieval. Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer |
| 2018 | Lipschitz Continuity in Model-based Reinforcement Learning. Kavosh Asadi, Dipendra Misra, Michael L. Littman |
| 2018 | Local Convergence Properties of SAGA/Prox-SVRG and Acceleration. Clarice Poon, Jingwei Liang, Carola-Bibiane Schoenlieb |
| 2018 | Local Density Estimation in High Dimensions. Xian Wu, Moses Charikar, Vishnu Natchu |
| 2018 | Local Private Hypothesis Testing: Chi-Square Tests. Marco Gaboardi, Ryan Rogers |
| 2018 | Locally Private Hypothesis Testing. Or Sheffet |
| 2018 | Loss Decomposition for Fast Learning in Large Output Spaces. Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar |
| 2018 | Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering. Ahmed Douik, Babak Hassibi |
| 2018 | Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees. Adrien B. Taylor, Bryan Van Scoy, Laurent Lessard |
| 2018 | MAGAN: Aligning Biological Manifolds. Matthew Amodio, Smita Krishnaswamy |
| 2018 | MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk |
| 2018 | MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning. Bo Zhao, Xinwei Sun, Yanwei Fu, Yuan Yao, Yizhou Wang |
| 2018 | Machine Theory of Mind. Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew M. Botvinick |
| 2018 | Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits. Zeyuan Allen-Zhu, Sébastien Bubeck, Yuanzhi Li |
| 2018 | Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data. Minyoung Kim |
| 2018 | Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under 𝓁 Grigory Yaroslavtsev, Adithya Vadapalli |
| 2018 | Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order. Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li, David P. Woodruff, Lin F. Yang |
| 2018 | Max-Mahalanobis Linear Discriminant Analysis Networks. Tianyu Pang, Chao Du, Jun Zhu |
| 2018 | Mean Field Multi-Agent Reinforcement Learning. Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, Jun Wang |
| 2018 | Measuring abstract reasoning in neural networks. Adam Santoro, Felix Hill, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap |
| 2018 | MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels. Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei |
| 2018 | Message Passing Stein Variational Gradient Descent. Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang |
| 2018 | Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory. Ron Amit, Ron Meir |
| 2018 | Minibatch Gibbs Sampling on Large Graphical Models. Christopher De Sa, Vincent Chen, Wing Wong |
| 2018 | Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. Raj Agrawal, Caroline Uhler, Tamara Broderick |
| 2018 | Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Convariates. Xue Wang, Mike Mingcheng Wei, Tao Yao |
| 2018 | Mitigating Bias in Adaptive Data Gathering via Differential Privacy. Seth Neel, Aaron Roth |
| 2018 | Mix & Match Agent Curricula for Reinforcement Learning. Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu |
| 2018 | Mixed batches and symmetric discriminators for GAN training. Thomas Lucas, Corentin Tallec, Yann Ollivier, Jakob Verbeek |
| 2018 | Model-Level Dual Learning. Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu |
| 2018 | Modeling Others using Oneself in Multi-Agent Reinforcement Learning. Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus |
| 2018 | Modeling Sparse Deviations for Compressed Sensing using Generative Models. Manik Dhar, Aditya Grover, Stefano Ermon |
| 2018 | More Robust Doubly Robust Off-policy Evaluation. Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh |
| 2018 | Multi-Fidelity Black-Box Optimization with Hierarchical Partitions. Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai |
| 2018 | Multicalibration: Calibration for the (Computationally-Identifiable) Masses. Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum |
| 2018 | Mutual Information Neural Estimation. Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, Aaron C. Courville |
| 2018 | Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices. Zengfeng Huang |
| 2018 | Nearly Optimal Robust Subspace Tracking. Praneeth Narayanamurthy, Namrata Vaswani |
| 2018 | NetGAN: Generating Graphs via Random Walks. Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann |
| 2018 | Network Global Testing by Counting Graphlets. Jiashun Jin, Zheng Tracy Ke, Shengming Luo |
| 2018 | Neural Autoregressive Flows. Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron C. Courville |
| 2018 | Neural Dynamic Programming for Musical Self Similarity. Christian J. Walder, Dongwoo Kim |
| 2018 | Neural Inverse Rendering for General Reflectance Photometric Stereo. Tatsunori Taniai, Takanori Maehara |
| 2018 | Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions. Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein |
| 2018 | Neural Program Synthesis from Diverse Demonstration Videos. Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, Joseph J. Lim |
| 2018 | Neural Relational Inference for Interacting Systems. Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel |
| 2018 | Noise2Noise: Learning Image Restoration without Clean Data. Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila |
| 2018 | Noisin: Unbiased Regularization for Recurrent Neural Networks. Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei |
| 2018 | Noisy Natural Gradient as Variational Inference. Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse |
| 2018 | Non-Linear Motor Control by Local Learning in Spiking Neural Networks. Aditya Gilra, Wulfram Gerstner |
| 2018 | Non-convex Conditional Gradient Sliding. Chao Qu, Yan Li, Huan Xu |
| 2018 | Nonconvex Optimization for Regression with Fairness Constraints. Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao |
| 2018 | Nonoverlap-Promoting Variable Selection. Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric P. Xing |
| 2018 | Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski |
| 2018 | Nonparametric variable importance using an augmented neural network with multi-task learning. Jean Feng, Brian D. Williamson, Marco Carone, Noah Simon |
| 2018 | Not All Samples Are Created Equal: Deep Learning with Importance Sampling. Angelos Katharopoulos, François Fleuret |
| 2018 | Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care. Patrick Schwab, Emanuela Keller, Carl Muroi, David J. Mack, Christian Strässle, Walter Karlen |
| 2018 | Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. Anish Athalye, Nicholas Carlini, David A. Wagner |
| 2018 | On Acceleration with Noise-Corrupted Gradients. Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia |
| 2018 | On Learning Sparsely Used Dictionaries from Incomplete Samples. Thanh Van Nguyen, Akshay Soni, Chinmay Hegde |
| 2018 | On Matching Pursuit and Coordinate Descent. Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi |
| 2018 | On Nesting Monte Carlo Estimators. Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington |
| 2018 | On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. Risi Kondor, Shubhendu Trivedi |
| 2018 | On the Implicit Bias of Dropout. Poorya Mianjy, Raman Arora, René Vidal |
| 2018 | On the Limitations of First-Order Approximation in GAN Dynamics. Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt |
| 2018 | On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization. Sanjeev Arora, Nadav Cohen, Elad Hazan |
| 2018 | On the Power of Over-parametrization in Neural Networks with Quadratic Activation. Simon S. Du, Jason D. Lee |
| 2018 | On the Relationship between Data Efficiency and Error for Uncertainty Sampling. Stephen Mussmann, Percy Liang |
| 2018 | On the Spectrum of Random Features Maps of High Dimensional Data. Zhenyu Liao, Romain Couillet |
| 2018 | On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo. Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan |
| 2018 | One-Shot Segmentation in Clutter. Claudio Michaelis, Matthias Bethge, Alexander S. Ecker |
| 2018 | Online Convolutional Sparse Coding with Sample-Dependent Dictionary. Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. Ni |
| 2018 | Online Learning with Abstention. Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang |
| 2018 | Online Linear Quadratic Control. Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar |
| 2018 | Open Category Detection with PAC Guarantees. Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks |
| 2018 | Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods. Junhong Lin, Volkan Cevher |
| 2018 | Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces. Junhong Lin, Volkan Cevher |
| 2018 | Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data. Ganggang Xu, Zuofeng Shang, Guang Cheng |
| 2018 | Optimization Landscape and Expressivity of Deep CNNs. Quynh Nguyen, Matthias Hein |
| 2018 | Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization. Mark McLeod, Stephen J. Roberts, Michael A. Osborne |
| 2018 | Optimizing the Latent Space of Generative Networks. Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam |
| 2018 | Orthogonal Machine Learning: Power and Limitations. Ilias Zadik, Lester W. Mackey, Vasilis Syrgkanis |
| 2018 | Orthogonal Recurrent Neural Networks with Scaled Cayley Transform. Kyle Helfrich, Devin Willmott, Qiang Ye |
| 2018 | Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis. Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing |
| 2018 | Out-of-sample extension of graph adjacency spectral embedding. Keith D. Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe |
| 2018 | Overcoming Catastrophic Forgetting with Hard Attention to the Task. Joan Serrà, Didac Suris, Marius Miron, Alexandros Karatzoglou |
| 2018 | PDE-Net: Learning PDEs from Data. Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong |
| 2018 | PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya |
| 2018 | Parallel Bayesian Network Structure Learning. Tian Gao, Dennis Wei |
| 2018 | Parallel WaveNet: Fast High-Fidelity Speech Synthesis. Aäron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis C. Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, Demis Hassabis |
| 2018 | Parallel and Streaming Algorithms for K-Core Decomposition. Hossein Esfandiari, Silvio Lattanzi, Vahab S. Mirrokni |
| 2018 | Parameterized Algorithms for the Matrix Completion Problem. Robert Ganian, Iyad A. Kanj, Sebastian Ordyniak, Stefan Szeider |
| 2018 | Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering. Jan-Hendrik Lange, Andreas Karrenbauer, Bjoern Andres |
| 2018 | Path Consistency Learning in Tsallis Entropy Regularized MDPs. Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh |
| 2018 | Path-Level Network Transformation for Efficient Architecture Search. Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, Yong Yu |
| 2018 | Pathwise Derivatives Beyond the Reparameterization Trick. Martin Jankowiak, Fritz Obermeyer |
| 2018 | PixelSNAIL: An Improved Autoregressive Generative Model. Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel |
| 2018 | Policy Optimization as Wasserstein Gradient Flows. Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin |
| 2018 | Policy Optimization with Demonstrations. Bingyi Kang, Zequn Jie, Jiashi Feng |
| 2018 | Policy and Value Transfer in Lifelong Reinforcement Learning. David Abel, Yuu Jinnai, Yue (Sophie) Guo, George Dimitri Konidaris, Michael L. Littman |
| 2018 | Practical Contextual Bandits with Regression Oracles. Dylan J. Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, Robert E. Schapire |
| 2018 | PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning. Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jianmin Wang, Philip S. Yu |
| 2018 | Predict and Constrain: Modeling Cardinality in Deep Structured Prediction. Nataly Brukhim, Amir Globerson |
| 2018 | Prediction Rule Reshaping. Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty |
| 2018 | Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu |
| 2018 | Probabilistic Boolean Tensor Decomposition. Tammo Rukat, Christopher C. Holmes, Christopher Yau |
| 2018 | Probabilistic Recurrent State-Space Models. Andreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe |
| 2018 | Probably Approximately Metric-Fair Learning. Gal Yona, Guy N. Rothblum |
| 2018 | Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. Andrea Zanette, Emma Brunskill |
| 2018 | Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 Jennifer G. Dy, Andreas Krause |
| 2018 | Programmatically Interpretable Reinforcement Learning. Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri |
| 2018 | Progress & Compress: A scalable framework for continual learning. Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell |
| 2018 | Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity. Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi |
| 2018 | Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy. Shipra Agrawal, Morteza Zadimoghaddam, Vahab S. Mirrokni |
| 2018 | Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope. Eric Wong, J. Zico Kolter |
| 2018 | Provable Variable Selection for Streaming Features. Jing Wang, Jie Shen, Ping Li |
| 2018 | Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing-and Back. Elliot Meyerson, Risto Miikkulainen |
| 2018 | QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson |
| 2018 | QuantTree: Histograms for Change Detection in Multivariate Data Streams. Giacomo Boracchi, Diego Carrera, Cristiano Cervellera, Danilo Macciò |
| 2018 | Quasi-Monte Carlo Variational Inference. Alexander Buchholz, Florian Wenzel, Stephan Mandt |
| 2018 | Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. Heinrich Jiang, Jennifer Jang, Samory Kpotufe |
| 2018 | RLlib: Abstractions for Distributed Reinforcement Learning. Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael I. Jordan, Ion Stoica |
| 2018 | Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors. Yichi Zhou, Jun Zhu, Jingwei Zhuo |
| 2018 | RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks. Jinsung Yoon, James Jordon, Mihaela van der Schaar |
| 2018 | Randomized Block Cubic Newton Method. Nikita Doikov, Peter Richtárik |
| 2018 | Ranking Distributions based on Noisy Sorting. Adil El Mesaoudi-Paul, Eyke Hüllermeier, Róbert Busa-Fekete |
| 2018 | Rapid Adaptation with Conditionally Shifted Neurons. Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler |
| 2018 | Rates of Convergence of Spectral Methods for Graphon Estimation. Jiaming Xu |
| 2018 | Rectify Heterogeneous Models with Semantic Mapping. Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou |
| 2018 | Recurrent Predictive State Policy Networks. Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha S. Srinivasa, Geoffrey J. Gordon |
| 2018 | Regret Minimization for Partially Observable Deep Reinforcement Learning. Peter H. Jin, Kurt Keutzer, Sergey Levine |
| 2018 | Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control. Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski |
| 2018 | Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training. Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha |
| 2018 | Representation Learning on Graphs with Jumping Knowledge Networks. Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka |
| 2018 | Representation Tradeoffs for Hyperbolic Embeddings. Frederic Sala, Christopher De Sa, Albert Gu, Christopher Ré |
| 2018 | Residual Unfairness in Fair Machine Learning from Prejudiced Data. Nathan Kallus, Angela Zhou |
| 2018 | Revealing Common Statistical Behaviors in Heterogeneous Populations. Andrey Zhitnikov, Rotem Mulayoff, Tomer Michaeli |
| 2018 | Reviving and Improving Recurrent Back-Propagation. Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel |
| 2018 | Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis. Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra |
| 2018 | Robust and Scalable Models of Microbiome Dynamics. Travis E. Gibson, Georg K. Gerber |
| 2018 | SADAGRAD: Strongly Adaptive Stochastic Gradient Methods. Zaiyi Chen, Yi Xu, Enhong Chen, Tianbao Yang |
| 2018 | SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate. Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan |
| 2018 | SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song |
| 2018 | SGD and Hogwild! Convergence Without the Bounded Gradients Assumption. Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác |
| 2018 | SIGNSGD: Compressed Optimisation for Non-Convex Problems. Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar |
| 2018 | SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions. Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang |
| 2018 | SQL-Rank: A Listwise Approach to Collaborative Ranking. Liwei Wu, Cho-Jui Hsieh, James Sharpnack |
| 2018 | Safe Element Screening for Submodular Function Minimization. Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang |
| 2018 | Scalable Approximate Bayesian Inference for Particle Tracking Data. Ruoxi Sun, Liam Paninski |
| 2018 | Scalable Bilinear Learning Using State and Action Features. Yichen Chen, Lihong Li, Mengdi Wang |
| 2018 | Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints. Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi |
| 2018 | Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF). Trefor W. Evans, Prasanth B. Nair |
| 2018 | Selecting Representative Examples for Program Synthesis. Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Pack Kaelbling |
| 2018 | Self-Bounded Prediction Suffix Tree via Approximate String Matching. Dongwoo Kim, Christian J. Walder |
| 2018 | Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings. John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine |
| 2018 | Self-Imitation Learning. Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee |
| 2018 | Semi-Amortized Variational Autoencoders. Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush |
| 2018 | Semi-Implicit Variational Inference. Mingzhang Yin, Mingyuan Zhou |
| 2018 | Semi-Supervised Learning on Data Streams via Temporal Label Propagation. Tal Wagner, Sudipto Guha, Shiva Prasad Kasiviswanathan, Nina Mishra |
| 2018 | Semi-Supervised Learning via Compact Latent Space Clustering. Konstantinos Kamnitsas, Daniel Coelho de Castro, Loïc Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya V. Nori |
| 2018 | Semiparametric Contextual Bandits. Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis |
| 2018 | Shampoo: Preconditioned Stochastic Tensor Optimization. Vineet Gupta, Tomer Koren, Yoram Singer |
| 2018 | Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit. Sreejith Kallummil, Sheetal Kalyani |
| 2018 | Smoothed Action Value Functions for Learning Gaussian Policies. Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans |
| 2018 | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine |
| 2018 | Solving Partial Assignment Problems using Random Clique Complexes. Charu Sharma, Deepak Nathani, Manohar Kaul |
| 2018 | Sound Abstraction and Decomposition of Probabilistic Programs. Steven Holtzen, Guy Van den Broeck, Todd D. Millstein |
| 2018 | SparseMAP: Differentiable Sparse Structured Inference. Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie |
| 2018 | Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection. Jeremias Knoblauch, Theodoros Damoulas |
| 2018 | Spectrally Approximating Large Graphs with Smaller Graphs. Andreas Loukas, Pierre Vandergheynst |
| 2018 | Spline Filters For End-to-End Deep Learning. Randall Balestriero, Romain Cosentino, Hervé Glotin, Richard G. Baraniuk |
| 2018 | Spotlight: Optimizing Device Placement for Training Deep Neural Networks. Yuanxiang Gao, Li Chen, Baochun Li |
| 2018 | Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. Itay Safran, Ohad Shamir |
| 2018 | Stability and Generalization of Learning Algorithms that Converge to Global Optima. Zachary Charles, Dimitris S. Papailiopoulos |
| 2018 | Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization. Jiong Zhang, Qi Lei, Inderjit S. Dhillon |
| 2018 | Stagewise Safe Bayesian Optimization with Gaussian Processes. Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue |
| 2018 | State Abstractions for Lifelong Reinforcement Learning. David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman |
| 2018 | State Space Gaussian Processes with Non-Gaussian Likelihood. Hannes Nickisch, Arno Solin, Alexander Grigorevskiy |
| 2018 | Stein Points. Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates |
| 2018 | Stein Variational Gradient Descent Without Gradient. Jun Han, Qiang Liu |
| 2018 | Stein Variational Message Passing for Continuous Graphical Models. Dilin Wang, Zhe Zeng, Qiang Liu |
| 2018 | Stochastic PCA with 𝓁 Poorya Mianjy, Raman Arora |
| 2018 | Stochastic Proximal Algorithms for AUC Maximization. Michael Natole, Yiming Ying, Siwei Lyu |
| 2018 | Stochastic Training of Graph Convolutional Networks with Variance Reduction. Jianfei Chen, Jun Zhu, Le Song |
| 2018 | Stochastic Variance-Reduced Cubic Regularized Newton Method. Dongruo Zhou, Pan Xu, Quanquan Gu |
| 2018 | Stochastic Variance-Reduced Hamilton Monte Carlo Methods. Difan Zou, Pan Xu, Quanquan Gu |
| 2018 | Stochastic Variance-Reduced Policy Gradient. Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli |
| 2018 | Stochastic Video Generation with a Learned Prior. Emily Denton, Rob Fergus |
| 2018 | Stochastic Wasserstein Barycenters. Sebastian Claici, Edward Chien, Justin Solomon |
| 2018 | StrassenNets: Deep Learning with a Multiplication Budget. Michael Tschannen, Aran Khanna, Animashree Anandkumar |
| 2018 | Streaming Principal Component Analysis in Noisy Settings. Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora |
| 2018 | Stronger Generalization Bounds for Deep Nets via a Compression Approach. Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang |
| 2018 | Structured Control Nets for Deep Reinforcement Learning. Mario Srouji, Jian Zhang, Ruslan Salakhutdinov |
| 2018 | Structured Evolution with Compact Architectures for Scalable Policy Optimization. Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller |
| 2018 | Structured Output Learning with Abstention: Application to Accurate Opinion Prediction. Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d'Alché-Buc |
| 2018 | Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors. Soumya Ghosh, Jiayu Yao, Finale Doshi-Velez |
| 2018 | Structured Variationally Auto-encoded Optimization. Xiaoyu Lu, Javier González, Zhenwen Dai, Neil D. Lawrence |
| 2018 | Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis. Yuxuan Wang, Daisy Stanton, Yu Zhang, R. J. Skerry-Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Ye Jia, Fei Ren, Rif A. Saurous |
| 2018 | Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering. Pan Li, Olgica Milenkovic |
| 2018 | Subspace Embedding and Linear Regression with Orlicz Norm. Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong |
| 2018 | Synthesizing Programs for Images using Reinforced Adversarial Learning. Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals |
| 2018 | Synthesizing Robust Adversarial Examples. Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok |
| 2018 | TACO: Learning Task Decomposition via Temporal Alignment for Control. Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, Ingmar Posner |
| 2018 | TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade |
| 2018 | Tempered Adversarial Networks. Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf |
| 2018 | Temporal Poisson Square Root Graphical Models. Sinong Geng, Zhaobin Kuang, Peggy L. Peissig, David Page |
| 2018 | Testing Sparsity over Known and Unknown Bases. Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal |
| 2018 | The Dynamics of Learning: A Random Matrix Approach. Zhenyu Liao, Romain Couillet |
| 2018 | The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang |
| 2018 | The Generalization Error of Dictionary Learning with Moreau Envelopes. Alexandros Georgogiannis |
| 2018 | The Hidden Vulnerability of Distributed Learning in Byzantium. El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault |
| 2018 | The Hierarchical Adaptive Forgetting Variational Filter. Vincent Moens |
| 2018 | The Limits of Maxing, Ranking, and Preference Learning. Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar |
| 2018 | The Mechanics of n-Player Differentiable Games. David Balduzzi, Sébastien Racanière, James Martens, Jakob N. Foerster, Karl Tuyls, Thore Graepel |
| 2018 | The Mirage of Action-Dependent Baselines in Reinforcement Learning. George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine |
| 2018 | The Multilinear Structure of ReLU Networks. Thomas Laurent, James von Brecht |
| 2018 | The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning. Siyuan Ma, Raef Bassily, Mikhail Belkin |
| 2018 | The Uncertainty Bellman Equation and Exploration. Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih |
| 2018 | The Weighted Kendall and High-order Kernels for Permutations. Yunlong Jiao, Jean-Philippe Vert |
| 2018 | The Well-Tempered Lasso. Yuanzhi Li, Yoram Singer |
| 2018 | Theoretical Analysis of Image-to-Image Translation with Adversarial Learning. Xudong Pan, Mi Zhang, Daizong Ding |
| 2018 | Theoretical Analysis of Sparse Subspace Clustering with Missing Entries. Manolis C. Tsakiris, René Vidal |
| 2018 | Thompson Sampling for Combinatorial Semi-Bandits. Siwei Wang, Wei Chen |
| 2018 | Tight Regret Bounds for Bayesian Optimization in One Dimension. Jonathan Scarlett |
| 2018 | Tighter Variational Bounds are Not Necessarily Better. Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh |
| 2018 | Time Limits in Reinforcement Learning. Fabio Pardo, Arash Tavakoli, Vitaly Levdik, Petar Kormushev |
| 2018 | To Understand Deep Learning We Need to Understand Kernel Learning. Mikhail Belkin, Siyuan Ma, Soumik Mandal |
| 2018 | Topological Mixture Estimation. Steve Huntsman |
| 2018 | Towards Binary-Valued Gates for Robust LSTM Training. Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu |
| 2018 | Towards Black-box Iterative Machine Teaching. Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song |
| 2018 | Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron. R. J. Skerry-Ryan, Eric Battenberg, Ying Xiao, Yuxuan Wang, Daisy Stanton, Joel Shor, Ron J. Weiss, Rob Clark, Rif A. Saurous |
| 2018 | Towards Fast Computation of Certified Robustness for ReLU Networks. Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane S. Boning, Inderjit S. Dhillon |
| 2018 | Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication. Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian |
| 2018 | Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings. Aviral Kumar, Sunita Sarawagi, Ujjwal Jain |
| 2018 | Training Neural Machines with Trace-Based Supervision. Matthew Mirman, Dimitar Dimitrov, Pavle Djordjevic, Timon Gehr, Martin T. Vechev |
| 2018 | Transfer Learning via Learning to Transfer. Ying Wei, Yu Zhang, Junzhou Huang, Qiang Yang |
| 2018 | Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos |
| 2018 | Transformation Autoregressive Networks. Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider |
| 2018 | Tree Edit Distance Learning via Adaptive Symbol Embeddings. Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer |
| 2018 | Tropical Geometry of Deep Neural Networks. Liwen Zhang, Gregory Naitzat, Lek-Heng Lim |
| 2018 | Unbiased Objective Estimation in Predictive Optimization. Shinji Ito, Akihiro Yabe, Ryohei Fujimaki |
| 2018 | Understanding Generalization and Optimization Performance of Deep CNNs. Pan Zhou, Jiashi Feng |
| 2018 | Understanding and Simplifying One-Shot Architecture Search. Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc V. Le |
| 2018 | Understanding the Loss Surface of Neural Networks for Binary Classification. Shiyu Liang, Ruoyu Sun, Yixuan Li, Rayadurgam Srikant |
| 2018 | Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control. Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn |
| 2018 | Using Inherent Structures to design Lean 2-layer RBMs. Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya |
| 2018 | Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning. Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Anthony Valenzano, Sheila A. McIlraith |
| 2018 | Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach. Mao Ye, Yan Sun |
| 2018 | Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization. Hang Wu, May D. Wang |
| 2018 | Variational Bayesian dropout: pitfalls and fixes. Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani |
| 2018 | Variational Inference and Model Selection with Generalized Evidence Bounds. Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin |
| 2018 | Variational Network Inference: Strong and Stable with Concrete Support. Amir Dezfouli, Edwin V. Bonilla, Richard Nock |
| 2018 | Video Prediction with Appearance and Motion Conditions. Yunseok Jang, Gunhee Kim, Yale Song |
| 2018 | Visualizing and Understanding Atari Agents. Samuel Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern |
| 2018 | WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models. Marine Le Morvan, Jean-Philippe Vert |
| 2018 | WSNet: Compact and Efficient Networks Through Weight Sampling. Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan |
| 2018 | Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer. Alexey Drutsa |
| 2018 | Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? Lin Chen, Moran Feldman, Amin Karbasi |
| 2018 | Weightless: Lossy weight encoding for deep neural network compression. Brandon Reagen, Udit Gupta, Bob Adolf, Michael Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks |
| 2018 | Which Training Methods for GANs do actually Converge? Lars M. Mescheder, Andreas Geiger, Sebastian Nowozin |
| 2018 | Yes, but Did It Work?: Evaluating Variational Inference. Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman |
| 2018 | oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis. Samuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox |
| 2018 | prDeep: Robust Phase Retrieval with a Flexible Deep Network. Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan, Richard G. Baraniuk |