ICML 2018
621 papers
$d^2$: Decentralized Training over Decentralized Data
Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu A Delay-Tolerant Proximal-Gradient Algorithm for Distributed Learning
Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick, Massih-Reza Amini A Distributed Second-Order Algorithm You Can Trust
Celestine Duenner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi A Progressive Batching L-BFGS Method for Machine Learning
Raghu Bollapragada, Jorge Nocedal, Dheevatsa Mudigere, Hao-Jun Shi, Ping Tak Peter Tang A Reductions Approach to Fair Classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach Accelerated Spectral Ranking
Arpit Agarwal, Prathamesh Patil, Shivani Agarwal Accurate Inference for Adaptive Linear Models
Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy Active Learning with Logged Data
Songbai Yan, Kamalika Chaudhuri, Tara Javidi Adaptive Three Operator Splitting
Fabian Pedregosa, Gauthier Gidel Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song Adversarial Distillation of Bayesian Neural Network Posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel Adversarial Learning with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan Adversarial Regression with Multiple Learners
Liang Tong, Sixie Yu, Scott Alfeld, Vorobeychik Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin Duke, Ricardo Henao Adversarially Regularized Autoencoders
Junbo Zhao, Yoon Kim, Kelly Zhang, Alexander Rush, Yann LeCun An Efficient, Generalized Bellman Update for Cooperative Inverse Reinforcement Learning
Dhruv Malik, Malayandi Palaniappan, Jaime Fisac, Dylan Hadfield-Menell, Stuart Russell, Anca Dragan Analyzing Uncertainty in Neural Machine Translation
Myle Ott, Michael Auli, David Grangier, Marc’Aurelio Ranzato Anonymous Walk Embeddings
Sergey Ivanov, Evgeny Burnaev Asynchronous Byzantine Machine Learning (the Case of SGD)
Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Rhicheek Patra, Mahsa Taziki Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Umut Simsekli, Cagatay Yildiz, Than Huy Nguyen, Taylan Cemgil, Gael Richard Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
Amjad Almahairi, Sai Rajeshwar, Alessandro Sordoni, Philip Bachman, Aaron Courville Bandits with Delayed, Aggregated Anonymous Feedback
Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari, Steffen Grunewalder Bayesian Model Selection for Change Point Detection and Clustering
Othmane Mazhar, Cristian Rojas, Carlo Fischione, Mohammad Reza Hesamzadeh Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter, Jane Wang, Zeb Kurth-Nelson, Siddhant Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic, Amir Zandieh, Aidasadat Mousavifar, Ola Svensson Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil Binary Partitions with Approximate Minimum Impurity
Eduardo Laber, Marco Molinaro, Felipe Mello Pereira Black Box FDR
Wesley Tansey, Yixin Wang, David Blei, Raul Rabadan Blind Justice: Fairness with Encrypted Sensitive Attributes
Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, Adrian Weller Born Again Neural Networks
Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar Bounding and Counting Linear Regions of Deep Neural Networks
Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam Bucket Renormalization for Approximate Inference
Sungsoo Ahn, Michael Chertkov, Adrian Weller, Jinwoo Shin Budgeted Experiment Design for Causal Structure Learning
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu, Alex Irpan, Jacob Andreas, Bobby Kleinberg, Quoc Le, Jon Kleinberg Causal Bandits with Propagating Inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita, Shinji Ito, Naonori Kakimura, Takuro Fukunaga, Ken-ichi Kawarabayashi Chi-Square Generative Adversarial Network
Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin Duke Clipped Action Policy Gradient
Yasuhiro Fujita, Shin-ichi Maeda Coded Sparse Matrix Multiplication
Sinong Wang, Jiashang Liu, Ness Shroff Comparing Dynamics: Deep Neural Networks Versus Glassy Systems
Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gerard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli Comparison-Based Random Forests
Siavash Haghiri, Damien Garreau, Ulrike Luxburg Composable Planning with Attributes
Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus Conditional Neural Processes
Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, S. M. Ali Eslami Configurable Markov Decision Processes
Alberto Maria Metelli, Mirco Mutti, Marcello Restelli Constant-Time Predictive Distributions for Gaussian Processes
Geoff Pleiss, Jacob Gardner, Kilian Weinberger, Andrew Gordon Wilson Constrained Interacting Submodular Groupings
Andrew Cotter, Mahdi Milani Fard, Seungil You, Maya Gupta, Jeff Bilmes ContextNet: Deep Learning for Star Galaxy Classification
Noble Kennamer, David Kirkby, Alexander Ihler, Francisco Javier Sanchez-Lopez Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin Duke Convolutional Imputation of Matrix Networks
Qingyun Sun, Mengyuan Yan, David Donoho, Boyd Crowdsourcing with Arbitrary Adversaries
Matthaeus Kleindessner, Pranjal Awasthi CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi Deep Density Destructors
David Inouye, Pradeep Ravikumar Deep Models of Interactions Across Sets
Jason Hartford, Devon Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh Deep One-Class Classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft Deep Predictive Coding Network for Object Recognition
Haiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, Zhongming Liu Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson Delayed Impact of Fair Machine Learning
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt DiCE: The Infinitely Differentiable Monte Carlo Estimator
Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, Shimon Whiteson Differentiable Compositional Kernel Learning for Gaussian Processes
Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse Differentially Private Matrix Completion Revisited
Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, James Bailey Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Arash Vahdat, William Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash Dynamic Evaluation of Neural Sequence Models
Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals Dynamic Regret of Strongly Adaptive Methods
Lijun Zhang, Tianbao Yang, Jin, Zhi-Hua Zhou Efficient and Consistent Adversarial Bipartite Matching
Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian Ziebart Efficient Neural Audio Synthesis
Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron Oord, Sander Dieleman, Koray Kavukcuoglu End-to-End Active Object Tracking via Reinforcement Learning
Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang Escaping Saddles with Stochastic Gradients
Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi, Thomas Hofmann Essentially No Barriers in Neural Network Energy Landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred Hamprecht Fair and Diverse DPP-Based Data Summarization
Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, Nisheeth Vishnoi Fairness Without Demographics in Repeated Loss Minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava Fast Bellman Updates for Robust MDPs
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann Fast Decoding in Sequence Models Using Discrete Latent Variables
Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, Noam Shazeer Fast Information-Theoretic Bayesian Optimisation
Binxin Ru, Michael A. Osborne, Mark Mcleod, Diego Granziol Fast Parametric Learning with Activation Memorization
Jack Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, Tianbao Yang Feasible Arm Identification
Julian Katz-Samuels, Clay Scott First Order Generative Adversarial Networks
Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter Fixing a Broken ELBO
Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A. Saurous, Kevin Murphy Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Rosemary Ke, Konrad Żołna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Christopher Pal Fourier Policy Gradients
Matthew Fellows, Kamil Ciosek, Shimon Whiteson Frank-Wolfe with Subsampling Oracle
Thomas Kerdreux, Fabian Pedregosa, Alexandre d’Aspremont Gated Path Planning Networks
Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov Generative Temporal Models with Spatial Memory for Partially Observed Environments
Marco Fraccaro, Danilo Rezende, Yori Zwols, Alexander Pritzel, S. M. Ali Eslami, Fabio Viola Geodesic Convolutional Shape Optimization
Pierre Baque, Edoardo Remelli, Francois Fleuret, Pascal Fua Gradient Coding from Cyclic MDS Codes and Expander Graphs
Netanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo Graph Networks as Learnable Physics Engines for Inference and Control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia Hierarchical Clustering with Structural Constraints
Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar Hierarchical Imitation and Reinforcement Learning
Hoang Le, Nan Jiang, Alekh Agarwal, Miroslav Dudik, Yisong Yue, Hal Daumé Hierarchical Multi-Label Classification Networks
Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu Importance Weighted Transfer of Samples in Reinforcement Learning
Andrea Tirinzoni, Andrea Sessa, Matteo Pirotta, Marcello Restelli INSPECTRE: Privately Estimating the Unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang Investigating Human Priors for Playing Video Games
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Tom Griffiths, Alexei Efros Is Generator Conditioning Causally Related to GAN Performance?
Augustus Odena, Jacob Buckman, Catherine Olsson, Tom Brown, Christopher Olah, Colin Raffel, Ian Goodfellow Iterative Amortized Inference
Joe Marino, Yisong Yue, Stephan Mandt 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 Duke Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu Kernelized Synaptic Weight Matrices
Lorenz Muller, Julien Martel, Giacomo Indiveri Kronecker Recurrent Units
Cijo Jose, Moustapha Cisse, Francois Fleuret Latent Space Policies for Hierarchical Reinforcement Learning
Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, Sergey Levine Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel Learning by Playing Solving Sparse Reward Tasks from Scratch
Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg Learning Diffusion Using Hyperparameters
Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg Learning Independent Causal Mechanisms
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf Learning Memory Access Patterns
Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan Learning Policy Representations in Multiagent Systems
Aditya Grover, Maruan Al-Shedivat, Jayesh Gupta, Yuri Burda, Harrison Edwards Learning Representations and Generative Models for 3D Point Clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song Learning to Branch
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik Learning to Explore via Meta-Policy Gradient
Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng Learning to Optimize Combinatorial Functions
Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer Learning to Search with MCTSnets
Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Remi Munos, David Silver Learning Unknown ODE Models with Gaussian Processes
Markus Heinonen, Cagatay Yildiz, Henrik Mannerström, Jukka Intosalmi, Harri Lähdesmäki Learning with Abandonment
Sven Schmit, Ramesh Johari Let’s Be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher Loss Decomposition for Fast Learning in Large Output Spaces
Ian En-Hsu Yen, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar Machine Theory of Mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
Vladimir Braverman, Stephen Chestnut, Robert Krauthgamer, Yi Li, David Woodruff, Lin Yang Mean Field Multi-Agent Reinforcement Learning
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, Jun Wang Measuring Abstract Reasoning in Neural Networks
David Barrett, Felix Hill, Adam Santoro, Ari Morcos, Timothy Lillicrap Message Passing Stein Variational Gradient Descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang MISSION: Ultra Large-Scale Feature Selection Using Count-Sketches
Amirali Aghazadeh, Ryan Spring, Daniel Lejeune, Gautam Dasarathy, Anshumali Shrivastava, Baraniuk Mix & Match Agent Curricula for Reinforcement Learning
Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu Mixed Batches and Symmetric Discriminators for GAN Training
Thomas Lucas, Corentin Tallec, Yann Ollivier, Jakob Verbeek Model-Level Dual Learning
Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu More Robust Doubly Robust Off-Policy Evaluation
Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh Mutual Information Neural Estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, Devon Hjelm Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy, Namrata Vaswani NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann Neural Autoregressive Flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville Neural Program Synthesis from Diverse Demonstration Videos
Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, Joseph Lim Neural Relational Inference for Interacting Systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel Noise2Noise: Learning Image Restoration Without Clean Data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila Noisin: Unbiased Regularization for Recurrent Neural Networks
Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David Blei Noisy Natural Gradient as Variational Inference
Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse Nonoverlap-Promoting Variable Selection
Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric Xing Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patrick Schwab, Emanuela Keller, Carl Muroi, David J. Mack, Christian Strässle, Walter Karlen Oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Samuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox On Acceleration with Noise-Corrupted Gradients
Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia On Matching Pursuit and Coordinate Descent
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Raetsch, Bernhard Schölkopf, Sebastian Stich, Martin Jaggi On Nesting Monte Carlo Estimators
Tom Rainforth, Rob Cornish, Hongseok Yang, Andrew Warrington, Frank Wood On the Implicit Bias of Dropout
Poorya Mianjy, Raman Arora, Rene Vidal On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, Michael Jordan One-Shot Segmentation in Clutter
Claudio Michaelis, Matthias Bethge, Alexander Ecker Online Learning with Abstention
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang Online Linear Quadratic Control
Alon Cohen, Avinatan Hasidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar Open Category Detection with PAC Guarantees
Si Liu, Risheek Garrepalli, Thomas Dietterich, Alan Fern, Dan Hendrycks Optimizing the Latent Space of Generative Networks
Piotr Bojanowski, Armand Joulin, David Lopez-Pas, Arthur Szlam Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Aaron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George Driessche, Edward Lockhart, Luis 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 Parameterized Algorithms for the Matrix Completion Problem
Robert Ganian, Iyad Kanj, Sebastian Ordyniak, Stefan Szeider PDE-Net: Learning PDEs from Data
Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong PixelSNAIL: An Improved Autoregressive Generative Model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel Policy and Value Transfer in Lifelong Reinforcement Learning
David Abel, Yuu Jinnai, Sophie Yue Guo, George Konidaris, Michael Littman Policy Optimization as Wasserstein Gradient Flows
Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin Practical Contextual Bandits with Regression Oracles
Dylan Foster, Alekh Agarwal, Miroslav Dudik, Haipeng Luo, Robert Schapire prDeep: Robust Phase Retrieval with a Flexible Deep Network
Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan, Richard Baraniuk Prediction Rule Reshaping
Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty Probabilistic Boolean Tensor Decomposition
Tammo Rukat, Chris Holmes, Christopher Yau Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg, Nguyen-Tuong Duy, Stefan Schaal, Marc Toussaint, Trimpe Sebastian Programmatically Interpretable Reinforcement Learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri Progress & Compress: A Scalable Framework for Continual Learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, Shimon Whiteson Quasi-Monte Carlo Variational Inference
Alexander Buchholz, Florian Wenzel, Stephan Mandt Randomized Block Cubic Newton Method
Nikita Doikov, Peter Richtarik, University Edinburgh Ranking Distributions Based on Noisy Sorting
Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete Rapid Adaptation with Conditionally Shifted Neurons
Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler Rectify Heterogeneous Models with Semantic Mapping
Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, Zhi-Hua Zhou Recurrent Predictive State Policy Networks
Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka Representation Tradeoffs for Hyperbolic Embeddings
Frederic Sala, Chris De Sa, Albert Gu, Christopher Re Reviving and Improving Recurrent Back-Propagation
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, Ion Stoica SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song Selecting Representative Examples for Program Synthesis
Yewen Pu, Zachery Miranda, Armando Solar-Lezama, Leslie Kaelbling Self-Imitation Learning
Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee Semi-Amortized Variational Autoencoders
Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, Alexander Rush Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori Semiparametric Contextual Bandits
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Lam Nguyen, Phuong Ha Nguyen, Marten Dijk, Peter Richtarik, Katya Scheinberg, Martin Takac signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Chandrajit Bajaj, Tingran Gao, Zihang He, Qixing Huang, Zhenxiao Liang Smoothed Action Value Functions for Learning Gaussian Policies
Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae, Andre Martins, Mathieu Blondel, Claire Cardie Spline Filters for End-to-End Deep Learning
Randall Balestriero, Romain Cosentino, Herve Glotin, Richard Baraniuk State Abstractions for Lifelong Reinforcement Learning
David Abel, Dilip Arumugam, Lucas Lehnert, Michael Littman Stein Points
Wilson Ye Chen, Lester Mackey, Jackson Gorham, Francois-Xavier Briol, Chris Oates Stochastic Variance-Reduced Policy Gradient
Matteo Papini, Damiano Binaghi, Giuseppe Canonaco, Matteo Pirotta, Marcello Restelli Stochastic Wasserstein Barycenters
Sebastian Claici, Edward Chien, Justin Solomon Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard Turner, Adrian Weller Structured Variationally Auto-Encoded Optimization
Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil D. Lawrence Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ-Skerry Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Ye Jia, Fei Ren, Rif A. Saurous Subspace Embedding and Linear Regression with Orlicz Norm
Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong Synthesizing Programs for Images Using Reinforced Adversarial Learning
Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals Synthesizing Robust Adversarial Examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok TACO: Learning Task Decomposition via Temporal Alignment for Control
Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, Ingmar Posner Tempered Adversarial Networks
Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf Temporal Poisson Square Root Graphical Models
Sinong Geng, Zhaobin Kuang, Peggy Peissig, David Page Testing Sparsity over Known and Unknown Bases
Siddharth Barman, Arnab Bhattacharyya, Suprovat Ghoshal The Limits of Maxing, Ranking, and Preference Learning
Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar The Mechanics of N-Player Differentiable Games
David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard Turner, Zoubin Ghahramani, Sergey Levine The Uncertainty Bellman Equation and Exploration
Brendan O’Donoghue, Ian Osband, Remi Munos, Vlad Mnih The Well-Tempered Lasso
Yuanzhi Li, Yoram Singer Tighter Variational Bounds Are Not Necessarily Better
Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh Time Limits in Reinforcement Learning
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Liwen Zhang, Gregory Naitzat, Lek-Heng Lim Understanding and Simplifying One-Shot Architecture Search
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Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya Variational Inference and Model Selection with Generalized Evidence Bounds
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