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ICML 2019 Papers with Code

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519 out of 774 ICML 2019 papers have code published. We list all of them in the following table. Since the extraction step is done by machines, we may miss some papers. Let us know if more papers can be added to this table.

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Title Authors Code
1 AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Sch?lkopf, Stefan Bauer link
2 Dynamic Weights in Multi-Objective Deep Reinforcement Learning Axel Abels, Diederik Roijers, Tom Lenaerts, Ann Now?, Denis Steckelmacher link
3 MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan link
4 TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning Tameem Adel, Adrian Weller link
5 PAC Learnability of Node Functions in Networked Dynamical Systems Abhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikanti link
6 Static Automatic Batching In TensorFlow Ashish Agarwal link
7 Efficient Full-Matrix Adaptive Regularization Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang link
8 Fair Regression: Quantitative Definitions and Reduction-Based Algorithms Alekh Agarwal, Miroslav Dudik, Zhiwei Steven Wu link
9 Learning to Generalize from Sparse and Underspecified Rewards Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi link
10 The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick link
11 Understanding the Impact of Entropy on Policy Optimization Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans link
12 Fairwashing: the risk of rationalization Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, S?bastien Gambs, Satoshi Hara, Alain Tapp link
13 Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida link
14 Projections for Approximate Policy Iteration Algorithms Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann link
15 Multi-objective training of Generative Adversarial Networks with multiple discriminators Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk, Ioannis Mitliagkas link
16 Graph Element Networks: adaptive, structured computation and memory Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauza Villalonga, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Kaelbling link
17 Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, Michael A. Osborne link
18 Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation Marco Ancona, Cengiz Oztireli, Markus Gross link
19 Scaling Up Ordinal Embedding: A Landmark Approach Jesse Anderton, Javed Aslam link
20 Sorting Out Lipschitz Function Approximation Cem Anil, James Lucas, Roger Grosse link
21 Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi link
22 Unsupervised Label Noise Modeling and Loss Correction Eric Arazo, Diego Ortego, Paul Albert, Noel O?Connor, Kevin Mcguinness link
23 Stochastic Gradient Push for Distributed Deep Learning Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat link
24 Bayesian Optimization of Composite Functions Raul Astudillo, Peter Frazier link
25 Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra Slavkovic link
26 Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data Sergul Aydore, Bertrand Thirion, Gael Varoquaux link
27 Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior Fadhel Ayed, Juho Lee, Francois Caron link
28 Scalable Fair Clustering Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner link
29 Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi link
30 Provable Guarantees for Gradient-Based Meta-Learning Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar link
31 Concrete Autoencoders: Differentiable Feature Selection and Reconstruction Muhammed Fatih Balin, Abubakar Abid, James Zou link
32 HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving Kshitij Bansal, Sarah Loos, Markus Rabe, Christian Szegedy, Stewart Wilcox link
33 Learning to Route in Similarity Graphs Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko link
34 A Personalized Affective Memory Model for Improving Emotion Recognition Pablo Barros, German Parisi, Stefan Wermter link
35 Noise2Self: Blind Denoising by Self-Supervision Joshua Batson, Loic Royer link
36 Efficient optimization of loops and limits with randomized telescoping sums Alex Beatson, Ryan P Adams link
37 Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumann link
38 Active Learning for Probabilistic Structured Prediction of Cuts and Matchings Sima Behpour, Anqi Liu, Brian Ziebart link
39 Invertible Residual Networks Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen link
40 Greedy Layerwise Learning Can Scale To ImageNet Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon link
41 Overcoming Multi-model Forgetting Yassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat link
42 Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger link
43 Adversarially Learned Representations for Information Obfuscation and Inference Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, Guillermo Sapiro link
44 Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case Alina Beygelzimer, David Pal, Balazs Szorenyi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang link
45 Analyzing Federated Learning through an Adversarial Lens Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo link
46 Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference Yatao Bian, Joachim Buhmann, Andreas Krause link
47 More Efficient Off-Policy Evaluation through Regularized Targeted Learning Aurelien Bibaut, Ivana Malenica, Nikos Vlassis, Mark Van Der Laan link
48 A Kernel Perspective for Regularizing Deep Neural Networks Alberto Bietti, Gr?goire Mialon, Dexiong Chen, Julien Mairal link
49 Adversarial Attacks on Node Embeddings via Graph Poisoning Aleksandar Bojchevski, Stephan G?nnemann link
50 Online Variance Reduction with Mixtures Zal?n Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause link
51 Compositional Fairness Constraints for Graph Embeddings Avishek Bose, William Hamilton link
52 Unreproducible Research is Reproducible Xavier Bouthillier, C?sar Laurent, Pascal Vincent link
53 Blended Conditonal Gradients G?bor Braun, Sebastian Pokutta, Dan Tu, Stephen Wright link
54 Coresets for Ordered Weighted Clustering Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu link
55 Active Manifolds: A non-linear analogue to Active Subspaces Robert Bridges, Anthony Gruber, Christopher Felder, Miki Verma, Chelsey Hoff link
56 Conditioning by adaptive sampling for robust design David Brookes, Hahnbeom Park, Jennifer Listgarten link
57 Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum link
58 Understanding the Origins of Bias in Word Embeddings Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel link
59 Learning Generative Models across Incomparable Spaces Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka link
60 Rates of Convergence for Sparse Variational Gaussian Process Regression David Burt, Carl Edward Rasmussen, Mark Van Der Wilk link
61 A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent Yongqiang Cai, Qianxiao Li, Zuowei Shen link
62 Active Embedding Search via Noisy Paired Comparisons Gregory Canal, Andy Massimino, Mark Davenport, Christopher Rozell link
63 Dynamic Measurement Scheduling for Event Forecasting using Deep RL Chun-Hao Chang, Mingjie Mai, Anna Goldenberg link
64 On Symmetric Losses for Learning from Corrupted Labels Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama link
65 Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates George Chen link
66 Stein Point Markov Chain Monte Carlo Wilson Ye Chen, Alessandro Barp, Francois-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris Oates link
67 Particle Flow Bayes? Rule Xinshi Chen, Hanjun Dai, Le Song link
68 Proportionally Fair Clustering Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala link
69 Generative Adversarial User Model for Reinforcement Learning Based Recommendation System Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song link
70 Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels Pengfei Chen, Ben Ben Liao, Guangyong Chen, Shengyu Zhang link
71 A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng link
72 Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang link
73 Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse link
74 Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin link
75 Robust Decision Trees Against Adversarial Examples Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh link
76 RaFM: Rank-Aware Factorization Machines Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang link
77 Control Regularization for Reduced Variance Reinforcement Learning Richard Cheng, Abhinav Verma, Gabor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick link
78 Predictor-Corrector Policy Optimization Ching-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Boots link
79 Variational Inference for sparse network reconstruction from count data Julien Chiquet, Stephane Robin, Mahendra Mariadassou link
80 Random Walks on Hypergraphs with Edge-Dependent Vertex Weights Uthsav Chitra, Benjamin Raphael link
81 Neural Joint Source-Channel Coding Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon link
82 Beyond Backprop: Online Alternating Minimization with Auxiliary Variables Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf link
83 MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization Eric Chu, Peter Liu link
84 New results on information theoretic clustering Ferdinando Cicalese, Eduardo Laber, Lucas Murtinho link
85 Quantifying Generalization in Reinforcement Learning Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman link
86 Certified Adversarial Robustness via Randomized Smoothing Jeremy Cohen, Elan Rosenfeld, Zico Kolter link
87 CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning C?dric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, Mohamed Chetouani link
88 Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets Rob Cornish, Paul Vanetti, Alexandre Bouchard-Cote, George Deligiannidis, Arnaud Doucet link
89 Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You link
90 Monge blunts Bayes: Hardness Results for Adversarial Training Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder link
91 Boosted Density Estimation Remastered Zac Cranko, Richard Nock link
92 Matrix-Free Preconditioning in Online Learning Ashok Cutkosky, Tamas Sarlos link
93 Minimal Achievable Sufficient Statistic Learning Milan Cvitkovic, G?nther Koliander link
94 Open Vocabulary Learning on Source Code with a Graph-Structured Cache Milan Cvitkovic, Badal Singh, Animashree Anandkumar link
95 The Value Function Polytope in Reinforcement Learning Robert Dadashi, Marc G. Bellemare, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans link
96 Bayesian Optimization Meets Bayesian Optimal Stopping Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet link
97 Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Re link
98 A Kernel Theory of Modern Data Augmentation Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Re link
99 TarMAC: Targeted Multi-Agent Communication Abhishek Das, Th?ophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau link
100 Stochastic Deep Networks Gwendoline De Bie, Gabriel Peyr?, Marco Cuturi link
101 Learning-to-Learn Stochastic Gradient Descent with Biased Regularization Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil link
102 A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology Onur Dereli, Ceyda Oguz, Mehmet G?nen link
103 Learning to Convolve: A Generalized Weight-Tying Approach Nichita Diaconu, Daniel Worrall link
104 Sever: A Robust Meta-Algorithm for Stochastic Optimization Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart link
105 Approximated Oracle Filter Pruning for Destructive CNN Width Optimization Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan link
106 Noisy Dual Principal Component Pursuit Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel Robinson, Manolis Tsakiris, Rene Vidal link
107 Trajectory-Based Off-Policy Deep Reinforcement Learning Andreas Doerr, Michael Volpp, Marc Toussaint, Trimpe Sebastian, Christian Daniel link
108 Generalized No Free Lunch Theorem for Adversarial Robustness Elvis Dohmatob link
109 Provably efficient RL with Rich Observations via Latent State Decoding Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford link
110 Optimal Auctions through Deep Learning Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David Parkes, Sai Srivatsa Ravindranath link
111 Learning interpretable continuous-time models of latent stochastic dynamical systems Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani link
112 Autoregressive Energy Machines Conor Durkan, Charlie Nash link
113 Band-limited Training and Inference for Convolutional Neural Networks Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, Michael Franklin link
114 Imitating Latent Policies from Observation Ashley Edwards, Himanshu Sahni, Yannick Schroecker, Charles Isbell link
115 Semi-Cyclic Stochastic Gradient Descent Hubert Eichner, Tomer Koren, Brendan Mcmahan, Nathan Srebro, Kunal Talwar link
116 GDPP: Learning Diverse Generations using Determinantal Point Processes Mohamed Elfeki, Camille Couprie, Morgane Riviere, Mohamed Elhoseiny link
117 Improved Convergence for $\ell_1$ and $\ell_8$ Regression via Iteratively Reweighted Least Squares Alina Ene, Adrian Vladu link
118 Exploring the Landscape of Spatial Robustness Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry link
119 Cross-Domain 3D Equivariant Image Embeddings Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia link
120 On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schoenlieb link
121 Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam link
122 Dead-ends and Secure Exploration in Reinforcement Learning Mehdi Fatemi, Shikhar Sharma, Harm Van Seijen, Samira Ebrahimi Kahou link
123 DL2: Training and Querying Neural Networks with Logic Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin Vechev link
124 Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning Jakob Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling link
125 Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap Edwin Fong, Simon Lyddon, Chris Holmes link
126 On discriminative learning of prediction uncertainty Vojtech Franc, Daniel Prusa link
127 Learning Discrete Structures for Graph Neural Networks Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He link
128 Approximating Orthogonal Matrices with Effective Givens Factorization Thomas Frerix, Joan Bruna link
129 Fast and Flexible Inference of Joint Distributions from their Marginals Charlie Frogner, Tomaso Poggio link
130 Analyzing and Improving Representations with the Soft Nearest Neighbor Loss Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton link
131 Diagnosing Bottlenecks in Deep Q-learning Algorithms Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine link
132 MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin link
133 Off-Policy Deep Reinforcement Learning without Exploration Scott Fujimoto, David Meger, Doina Precup link
134 Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation Shani Gamrian, Yoav Goldberg link
135 Graph U-Nets Hongyang Gao, Shuiwang Ji link
136 Deep Generative Learning via Variational Gradient Flow Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang link
137 Rate Distortion For Model Compression:From Theory To Practice Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh link
138 Optimal Mini-Batch and Step Sizes for SAGA Nidham Gazagnadou, Robert Gower, Joseph Salmon link
139 SelectiveNet: A Deep Neural Network with an Integrated Reject Option Yonatan Geifman, Ran El-Yaniv link
140 DeepMDP: Learning Continuous Latent Space Models for Representation Learning Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare link
141 Learning and Data Selection in Big Datasets Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione, Mikael Skoglund link
142 An Instability in Variational Inference for Topic Models Behrooz Ghorbani, Hamid Javadi, Andrea Montanari link
143 An Investigation into Neural Net Optimization via Hessian Eigenvalue Density Behrooz Ghorbani, Shankar Krishnan, Ying Xiao link
144 Data Shapley: Equitable Valuation of Data for Machine Learning Amirata Ghorbani, James Zou link
145 Learning to Groove with Inverse Sequence Transformations Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman link
146 Adversarial Examples Are a Natural Consequence of Test Error in Noise Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin Cubuk link
147 Discovering Conditionally Salient Features with Statistical Guarantees Jaime Roquero Gimenez, James Zou link
148 Amortized Monte Carlo Integration Adam Golinski, Frank Wood, Tom Rainforth link
149 Efficient Training of BERT by Progressively Stacking Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tieyan Liu link
150 Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization Chengyue Gong, Jian Peng, Qiang Liu link
151 Obtaining Fairness using Optimal Transport Theory Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, Loubes Jean-Michel link
152 A Statistical Investigation of Long Memory in Language and Music Alexander Greaves-Tunnell, Zaid Harchaoui link
153 Automatic Posterior Transformation for Likelihood-Free Inference David Greenberg, Marcel Nonnenmacher, Jakob Macke link
154 Graphite: Iterative Generative Modeling of Graphs Aditya Grover, Aaron Zweig, Stefano Ermon link
155 Fast Algorithm for Generalized Multinomial Models with Ranking Data Jiaqi Gu, Guosheng Yin link
156 Towards a Deep and Unified Understanding of Deep Neural Models in NLP Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie link
157 An Investigation of Model-Free Planning Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap link
158 Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai link
159 Simple Black-box Adversarial Attacks Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, Kilian Weinberger link
160 Exploring interpretable LSTM neural networks over multi-variable data Tian Guo, Tao Lin, Nino Antulov-Fantulin link
161 Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs Lingbing Guo, Zequn Sun, Wei Hu link
162 Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications Albert Gural, Boris Murmann link
163 IMEXnet A Forward Stable Deep Neural Network Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto link
164 On The Power of Curriculum Learning in Training Deep Networks Guy Hacohen, Daphna Weinshall link
165 Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck Cadambe link
166 Learning Latent Dynamics for Planning from Pixels Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson link
167 Neural Separation of Observed and Unobserved Distributions Tavi Halperin, Ariel Ephrat, Yedid Hoshen link
168 Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning Seungyul Han, Youngchul Sung link
169 Importance Sampling Policy Evaluation with an Estimated Behavior Policy Josiah Hanna, Scott Niekum, Peter Stone link
170 Doubly-Competitive Distribution Estimation Yi Hao, Alon Orlitsky link
171 Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications Chris Harshaw, Moran Feldman, Justin Ward, Amin Karbasi link
172 Submodular Observation Selection and Information Gathering for Quadratic Models Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu link
173 Understanding and Controlling Memory in Recurrent Neural Networks Doron Haviv, Alexander Rivkind, Omri Barak link
174 On the Impact of the Activation function on Deep Neural Networks Training Soufiane Hayou, Arnaud Doucet, Judith Rousseau link
175 Provably Efficient Maximum Entropy Exploration Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest link
176 On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning Hoda Heidari, Vedant Nanda, Krishna Gummadi link
177 Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks, Kimin Lee, Mantas Mazeika link
178 Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel link
179 Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel link
180 Collective Model Fusion for Multiple Black-Box Experts Minh Hoang, Nghia Hoang, Bryan Kian Hsiang Low, Carleton Kingsford link
181 Connectivity-Optimized Representation Learning via Persistent Homology Christoph Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit link
182 Better generalization with less data using robust gradient descent Matthew Holland, Kazushi Ikeda link
183 Emerging Convolutions for Generative Normalizing Flows Emiel Hoogeboom, Rianne Van Den Berg, Max Welling link
184 Nonconvex Variance Reduced Optimization with Arbitrary Sampling Samuel Horv?th, Peter Richtarik link
185 Parameter-Efficient Transfer Learning for NLP Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly link
186 Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P R Kumar link
187 Classification from Positive, Unlabeled and Biased Negative Data Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama link
188 Bayesian Deconditional Kernel Mean Embeddings Kelvin Hsu, Fabio Ramos link
189 Unsupervised Deep Learning by Neighbourhood Discovery Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu link
190 Hierarchical Importance Weighted Autoencoders Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville link
191 Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour link
192 Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo, Mark Van Der Wilk, James Hensman, Carl Edward Rasmussen link
193 Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! Niels Ipsen, Lars Kai Hansen link
194 Actor-Attention-Critic for Multi-Agent Reinforcement Learning Shariq Iqbal, Fei Sha link
195 Complementary-Label Learning for Arbitrary Losses and Models Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama link
196 Sum-of-Squares Polynomial Flow Priyank Jaini, Kira A. Selby, Yaoliang Yu link
197 Learning What and Where to Transfer Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin link
198 Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega, Dj Strouse, Joel Z Leibo, Nando De Freitas link
199 A Deep Reinforcement Learning Perspective on Internet Congestion Control Nathan Jay, Noga Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar link
200 Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Juhan Nam link
201 Ladder Capsule Network Taewon Jeong, Youngmin Lee, Heeyoung Kim link
202 Training CNNs with Selective Allocation of Channels Jongheon Jeong, Jinwoo Shin link
203 Learning Discrete and Continuous Factors of Data via Alternating Disentanglement Yeonwoo Jeong, Hyun Oh Song link
204 Neural Logic Reinforcement Learning Zhengyao Jiang, Shan Luo link
205 Finding Options that Minimize Planning Time Yuu Jinnai, David Abel, David Hershkowitz, Michael Littman, George Konidaris link
206 Discovering Options for Exploration by Minimizing Cover Time Yuu Jinnai, Jee Won Park, David Abel, George Konidaris link
207 Kernel Mean Matching for Content Addressability of GANs Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Sch?lkopf link
208 GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver David John, Vincent Heuveline, Michael Schober link
209 Bilinear Bandits with Low-rank Structure Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak link
210 Statistical Foundations of Virtual Democracy Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, Christos-Alexandros Psomas link
211 Classifying Treatment Responders Under Causal Effect Monotonicity Nathan Kallus link
212 Trainable Decoding of Sets of Sequences for Neural Sequence Models Ashwin Kalyan, Peter Anderson, Stefan Lee, Dhruv Batra link
213 Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos link
214 Policy Consolidation for Continual Reinforcement Learning Christos Kaplanis, Murray Shanahan, Claudia Clopath link
215 Error Feedback Fixes SignSGD and other Gradient Compression Schemes Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian Stich, Martin Jaggi link
216 Riemannian adaptive stochastic gradient algorithms on matrix manifolds Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra link
217 Neural Inverse Knitting: From Images to Manufacturing Instructions Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik link
218 Processing Megapixel Images with Deep Attention-Sampling Models Angelos Katharopoulos, Francois Fleuret link
219 Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi link
220 Adaptive Scale-Invariant Online Algorithms for Learning Linear Models Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth link
221 CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network Tom Kenter, Vincent Wan, Chun-An Chan, Rob Clark, Jakub Vit link
222 Collaborative Evolutionary Reinforcement Learning Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer link
223 Geometry Aware Convolutional Filters for Omnidirectional Images Representation Renata Khasanova, Pascal Frossard link
224 EMI: Exploration with Mutual Information Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song link
225 FloWaveNet : A Generative Flow for Raw Audio Sungwon Kim, Sang-Gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon link
226 Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, Gunhee Kim link
227 Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model Gi-Soo Kim, Myunghee Cho Paik link
228 Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables Friso Kingma, Pieter Abbeel, Jonathan Ho link
229 CompILE: Compositional Imitation Learning and Execution Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter Battaglia link
230 Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause link
231 AUC\textmu: A Performance Metric for Multi-Class Machine Learning Models Ross Kleiman, David Page link
232 Fair k-Center Clustering for Data Summarization Matth?us Kleindessner, Pranjal Awasthi, Jamie Morgenstern link
233 POPQORN: Quantifying Robustness of Recurrent Neural Networks Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin link
234 Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication Anastasia Koloskova, Sebastian Stich, Martin Jaggi link
235 Robust Learning from Untrusted Sources Nikola Konstantinov, Christoph Lampert link
236 Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement Wouter Kool, Herke Van Hoof, Max Welling link
237 LIT: Learned Intermediate Representation Training for Model Compression Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia link
238 Estimate Sequences for Variance-Reduced Stochastic Composite Optimization Andrei Kulunchakov, Julien Mairal link
239 Loss Landscapes of Regularized Linear Autoencoders Daniel Kunin, Jonathan Bloom, Aleksandrina Goeva, Cotton Seed link
240 Geometry and Symmetry in Short-and-Sparse Deconvolution Han-Wen Kuo, Yenson Lau, Yuqian Zhang, John Wright link
241 A Large-Scale Study on Regularization and Normalization in GANs Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly link
242 Making Decisions that Reduce Discriminatory Impacts Matt Kusner, Chris Russell, Joshua Loftus, Ricardo Silva link
243 Characterizing Well-Behaved vs. Pathological Deep Neural Networks Antoine Labatie link
244 A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion Sylvain Lamprier link
245 Projection onto Minkowski Sums with Application to Constrained Learning Kenneth Lange, Joong-Ho Won, Jason Xu link
246 Safe Policy Improvement with Baseline Bootstrapping Romain Laroche, Paul Trichelair, Remi Tachet Des Combes link
247 Batch Policy Learning under Constraints Hoang Le, Cameron Voloshin, Yisong Yue link
248 Self-Attention Graph Pooling Junhyun Lee, Inyeop Lee, Jaewoo Kang link
249 Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, Yee Whye Teh link
250 Robust Inference via Generative Classifiers for Handling Noisy Labels Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin link
251 Sublinear Time Nearest Neighbor Search over Generalized Weighted Space Yifan Lei, Qiang Huang, Mohan Kankanhalli, Anthony Tung link
252 MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means Matthieu Lerasle, Zoltan Szabo, Timoth?e Mathieu, Guillaume Lecue link
253 Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group Mario Lezcano-Casado, David Marti?nez-Rubio link
254 Are Generative Classifiers More Robust to Adversarial Attacks? Yingzhen Li, John Bradshaw, Yash Sharma link
255 LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning Huaiyu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu link
256 Area Attention Yang Li, Lukasz Kaiser, Samy Bengio, Si Si link
257 Online Learning to Rank with Features Shuai Li, Tor Lattimore, Csaba Szepesvari link
258 NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong link
259 Bayesian Joint Spike-and-Slab Graphical Lasso Zehang Li, Tyler Mccormick, Samuel Clark link
260 Exploiting Worker Correlation for Label Aggregation in Crowdsourcing Yuan Li, Benjamin Rubinstein, Trevor Cohn link
261 Towards a Unified Analysis of Random Fourier Features Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic link
262 Feature-Critic Networks for Heterogeneous Domain Generalization Yiying Li, Yongxin Yang, Wei Zhou, Timothy Hospedales link
263 Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong link
264 Alternating Minimizations Converge to Second-Order Optimal Solutions Qiuwei Li, Zhihui Zhu, Gongguo Tang link
265 Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints Nikolaos Liakopoulos, Apostolos Destounis, Georgios Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos link
266 Regularization in directable environments with application to Tetris Jan Lichtenberg, Simsek link
267 Inference and Sampling of $K_33$-free Ising Models Valerii Likhosherstov, Yury Maximov, Misha Chertkov link
268 Kernel-Based Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim, Arnaud Autef link
269 Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt link
270 Acceleration of SVRG and Katyusha X by Inexact Preconditioning Yanli Liu, Fei Feng, Wotao Yin link
271 Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan link
272 Rao-Blackwellized Stochastic Gradients for Discrete Distributions Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael Jordan, Jon Mcauliffe link
273 Sparse Extreme Multi-label Learning with Oracle Property Weiwei Liu, Xiaobo Shen link
274 Data Poisoning Attacks on Stochastic Bandits Fang Liu, Ness Shroff link
275 The Implicit Fairness Criterion of Unconstrained Learning Lydia T. Liu, Max Simchowitz, Moritz Hardt link
276 Taming MAML: Efficient unbiased meta-reinforcement learning Hao Liu, Richard Socher, Caiming Xiong link
277 Understanding and Accelerating Particle-Based Variational Inference Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu link
278 Understanding MCMC Dynamics as Flows on the Wasserstein Space Chang Liu, Jingwei Zhuo, Jun Zhu link
279 Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert St?ter link
280 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Sch?lkopf, Olivier Bachem link
281 Neurally-Guided Structure Inference Sidi Lu, Jiayuan Mao, Joshua Tenenbaum, Jiajun Wu link
282 Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang link
283 CoT: Cooperative Training for Generative Modeling of Discrete Data Sidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Weinan Zhang link
284 Generalized Approximate Survey Propagation for High-Dimensional Estimation Carlo Lucibello, Luca Saglietti, Yue Lu link
285 High-Fidelity Image Generation With Fewer Labels Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly link
286 Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto link
287 Differentiable Dynamic Normalization for Learning Deep Representation Ping Luo, Peng Zhanglin, Shao Wenqi, Zhang Ruimao, Ren Jiamin, Wu Lingyun link
288 Variational Implicit Processes Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato link
289 EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, Cheng Zhang link
290 Bayesian leave-one-out cross-validation for large data M?ns Magnusson, Michael Andersen, Johan Jonasson, Aki Vehtari link
291 Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm Sepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan, Alireza Rezaei link
292 Guided evolutionary strategies: augmenting random search with surrogate gradients Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein link
293 Traditional and Heavy Tailed Self Regularization in Neural Network Models Michael Mahoney, Charles Martin link
294 Curvature-Exploiting Acceleration of Elastic Net Computations Vien Mai, Mikael Johansson link
295 Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms Ashok Makkuva, Pramod Viswanath, Sreeram Kannan, Sewoong Oh link
296 Calibrated Model-Based Deep Reinforcement Learning Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon link
297 Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborova link
298 Adversarial Generation of Time-Frequency Features with application in audio synthesis Andr?s Marafioti, Nathana?l Perraudin, Nicki Holighaus, Piotr Majdak link
299 Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models Kaspar Martens, Kieran Campbell, Christopher Yau link
300 Fairness-Aware Learning for Continuous Attributes and Treatments Jeremie Mary, Cl?ment Calauz?nes, Noureddine El Karoui link
301 Optimal Minimal Margin Maximization with Boosting Alexander Mathiasen, Kasper Green Larsen, Allan Gr?nlund link
302 Disentangling Disentanglement in Variational Autoencoders Emile Mathieu, Tom Rainforth, N Siddharth, Yee Whye Teh link
303 MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets Pierre-Alexandre Mattei, Jes Frellsen link
304 Graphical-model based estimation and inference for differential privacy Ryan Mckenna, Daniel Sheldon, Gerome Miklau link
305 Toward Controlling Discrimination in Online Ad Auctions Anay Mehrotra, Elisa Celis, Nisheeth Vishnoi link
306 Stochastic Blockmodels meet Graph Neural Networks Nikhil Mehta, Lawrence Carin Duke, Piyush Rai link
307 Imputing Missing Events in Continuous-Time Event Streams Hongyuan Mei, Guanghui Qin, Jason Eisner link
308 Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization Eldad Meller, Alexander Finkelstein, Uri Almog, Mark Grobman link
309 Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks Charith Mendis, Alex Renda, Dr.Saman Amarasinghe, Michael Carbin link
310 Geometric Losses for Distributional Learning Arthur Mensch, Mathieu Blondel, Gabriel Peyr? link
311 Spectral Clustering of Signed Graphs via Matrix Power Means Pedro Mercado, Francesco Tudisco, Matthias Hein link
312 Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization Michael Metel, Akiko Takeda link
313 Reinforcement Learning in Configurable Continuous Environments Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli link
314 Understanding and correcting pathologies in the training of learned optimizers Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-Dickstein link
315 On Dropout and Nuclear Norm Regularization Poorya Mianjy, Raman Arora link
316 Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography Andrew Miller, Ziad Obermeyer, John Cunningham, Sendhil Mullainathan link
317 Flat Metric Minimization with Applications in Generative Modeling Thomas M?llenhoff, Daniel Cremers link
318 Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization Seungyong Moon, Gaon An, Hyun Oh Song link
319 Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization Hesham Mostafa, Xin Wang link
320 Relational Pooling for Graph Representations Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro link
321 A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama link
322 Dropout as a Structured Shrinkage Prior Eric Nalisnick, Jose Miguel Hernandez-Lobato, Padhraic Smyth link
323 Learning Context-dependent Label Permutations for Multi-label Classification Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencia, Sunghyun Park, Ruhi Sarikaya, Johannes F?rnkranz link
324 A Framework for Bayesian Optimization in Embedded Subspaces Amin Nayebi, Alexander Munteanu, Matthias Poloczek link
325 Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani link
326 Safe Grid Search with Optimal Complexity Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi link
327 Learning to bid in revenue-maximizing auctions Thomas Nedelec, Noureddine El Karoui, Vianney Perchet link
328 Rotation Invariant Householder Parameterization for Bayesian PCA Rajbir Nirwan, Nils Bertschinger link
329 Lossless or Quantized Boosting with Integer Arithmetic Richard Nock, Robert Williamson link
330 Training Neural Networks with Local Error Signals Arild N?kland, Lars Hiller Eidnes link
331 Remember and Forget for Experience Replay Guido Novati, Petros Koumoutsakos link
332 Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama link
333 Tensor Variable Elimination for Plated Factor Graphs Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu, Alexander Rush, Noah Goodman link
334 Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models Michael Oberst, David Sontag link
335 TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing Augustus Odena, Catherine Olsson, David Andersen, Ian Goodfellow link
336 Orthogonal Random Forest for Causal Inference Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu link
337 Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding Muhammad Osama, Dave Zachariah, Thomas Sch?n link
338 Improving Adversarial Robustness via Promoting Ensemble Diversity Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu link
339 Nonparametric Bayesian Deep Networks with Local Competition Konstantinos Panousis, Sotirios Chatzis, Sergios Theodoridis link
340 Optimistic Policy Optimization via Multiple Importance Sampling Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli link
341 Deep Residual Output Layers for Neural Language Generation Nikolaos Pappas, James Henderson link
342 Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians Vardan Papyan link
343 Variational Laplace Autoencoders Yookoon Park, Chris Kim, Gunhee Kim link
344 Spectral Approximate Inference Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin link
345 Self-Supervised Exploration via Disagreement Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta link
346 Subspace Robust Wasserstein Distances Fran?ois-Pierre Paty, Marco Cuturi link
347 Domain Agnostic Learning with Disentangled Representations Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko link
348 Temporal Gaussian Mixture Layer for Videos Aj Piergiovanni, Michael Ryoo link
349 Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration Vladislav Polianskii, Florian T. Pokorny link
350 SAGA with Arbitrary Sampling Xun Qian, Zheng Qu, Peter Richtarik link
351 SGD with Arbitrary Sampling: General Analysis and Improved Rates Xun Qian, Peter Richtarik, Robert Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin link
352 AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson link
353 Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, Colin Raffel link
354 GMNN: Graph Markov Neural Networks Meng Qu, Yoshua Bengio, Jian Tang link
355 On the Spectral Bias of Neural Networks Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, Aaron Courville link
356 Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation Tahrima Rahman, Shasha Jin, Vibhav Gogate link
357 Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen link
358 Screening rules for Lasso with non-convex Sparse Regularizers Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon link
359 Topological Data Analysis of Decision Boundaries with Application to Model Selection Karthikeyan Natesan Ramamurthy, Kush Varshney, Krishnan Mody link
360 A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation Ramin Raziperchikolaei, Harish Bhat link
361 Do ImageNet Classifiers Generalize to ImageNet? Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar link
362 Almost Unsupervised Text to Speech and Automatic Speech Recognition Yi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu link
363 Adaptive Antithetic Sampling for Variance Reduction Hongyu Ren, Shengjia Zhao, Stefano Ermon link
364 A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes Alireza Rezaei, Shayan Oveis Gharan link
365 A Persistent Weisfeiler-Lehman Procedure for Graph Classification Bastian Rieck, Christian Bock, Karsten Borgwardt link
366 Separable value functions across time-scales Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Joelle Pineau, Emma Brunskill link
367 The Odds are Odd: A Statistical Test for Detecting Adversarial Examples Kevin Roth, Yannic Kilcher, Thomas Hofmann link
368 Neuron birth-death dynamics accelerates gradient descent and converges asymptotically Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden link
369 Iterative Linearized Control: Stable Algorithms and Complexity Guarantees Vincent Roulet, Dmitriy Drusvyatskiy, Siddhartha Srinivasa, Zaid Harchaoui link
370 A Contrastive Divergence for Combining Variational Inference and MCMC Francisco Ruiz, Michalis Titsias link
371 Plug-and-Play Methods Provably Converge with Properly Trained Denoisers Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin link
372 Tractable n-Metrics for Multiple Graphs Sam Safavi, Jose Bento link
373 An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule Touqir Sajed, Or Sheffet link
374 Deep Gaussian Processes with Importance-Weighted Variational Inference Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Deisenroth link
375 Locally Private Bayesian Inference for Count Models Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach link
376 Weakly-Supervised Temporal Localization via Occurrence Count Learning Julien Schroeter, Kirill Sidorov, David Marshall link
377 On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca Dragan link
378 Exploration Conscious Reinforcement Learning Revisited Lior Shani, Yonathan Efroni, Shie Mannor link
379 Mixture Models for Diverse Machine Translation: Tricks of the Trade Tianxiao Shen, Myle Ott, Michael Auli, Marc?Aurelio Ranzato link
380 Hessian Aided Policy Gradient Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi link
381 Learning with Bad Training Data via Iterative Trimmed Loss Minimization Yanyao Shen, Sujay Sanghavi link
382 Replica Conditional Sequential Monte Carlo Alex Shestopaloff, Arnaud Doucet link
383 Scalable Training of Inference Networks for Gaussian-Process Models Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu link
384 Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning Weishi Shi, Qi Yu link
385 Model-Based Active Exploration Pranav Shyam, Wojciech Jaskowski, Faustino Gomez link
386 Rehashing Kernel Evaluation in High Dimensions Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis link
387 First-Order Adversarial Vulnerability of Neural Networks and Input Dimension Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Sch?lkopf, David Lopez-Paz link
388 A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks Umut Simsekli, Levent Sagun, Mert Gurbuzbalaban link
389 Non-Parametric Priors For Generative Adversarial Networks Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin Braun link
390 Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi link
391 GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects Edward Smith, Adriana Romero, Scott Fujimoto, David Meger link
392 The Evolved Transformer David So, Quoc Le, Chen Liang link
393 QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, Yung Yi link
394 Distribution calibration for regression Hao Song, Tom Diethe, Meelis Kull, Peter Flach link
395 SELFIE: Refurbishing Unclean Samples for Robust Deep Learning Hwanjun Song, Minseok Kim, Jae-Gil Lee link
396 Revisiting the Softmax Bellman Operator: New Benefits and New Perspective Zhao Song, Ron Parr, Lawrence Carin link
397 Compressing Gradient Optimizers via Count-Sketches Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava link
398 Faster Attend-Infer-Repeat with Tractable Probabilistic Models Karl Stelzner, Robert Peharz, Kristian Kersting link
399 BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning Asa Cooper Stickland, Iain Murray link
400 Learning Optimal Linear Regularizers Matthew Streeter link
401 Learning Distance for Sequences by Learning a Ground Metric Bing Su, Ying Wu link
402 Contextual Memory Trees Wen Sun, Alina Beygelzimer, Hal Daum? Iii, John Langford, Paul Mineiro link
403 Provably Efficient Imitation Learning from Observation Alone Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell link
404 Active Learning for Decision-Making from Imbalanced Observational Data Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski link
405 Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness Raphael Suter, Djordje Miladinovic, Bernhard Sch?lkopf, Stefan Bauer link
406 Hyperbolic Disk Embeddings for Directed Acyclic Graphs Ryota Suzuki, Ryusuke Takahama, Shun Onoda link
407 Equivariant Transformer Networks Kai Sheng Tai, Peter Bailis, Gregory Valiant link
408 Making Deep Q-learning methods robust to time discretization Corentin Tallec, L?onard Blier, Yann Ollivier link
409 EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan, Quoc Le link
410 Hierarchical Decompositional Mixtures of Variational Autoencoders Ping Liang Tan, Robert Peharz link
411 Correlated Variational Auto-Encoders Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi link
412 The Variational Predictive Natural Gradient Da Tang, Rajesh Ranganath link
413 Adaptive Neural Trees Ryutaro Tanno, Kai Arulkumaran, Daniel Alexander, Antonio Criminisi, Aditya Nori link
414 Variational Annealing of GANs: A Langevin Perspective Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy Bobashev, Lawrence Carin Duke link
415 Predicate Exchange: Inference with Declarative Knowledge Zenna Tavares, Rajesh Ranganath, Javier Burroni, Armando Solar-Lezama, Edgar Minasyan link
416 The Natural Language of Actions Guy Tennenholtz, Shie Mannor link
417 Action Robust Reinforcement Learning and Applications in Continuous Control Chen Tessler, Yonathan Efroni, Shie Mannor link
418 Combating Label Noise in Deep Learning using Abstention Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof link
419 ELF OpenGo: an analysis and open reimplementation of AlphaZero Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick link
420 Random Matrix Improved Covariance Estimation for a Large Class of Metrics Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac link
421 Transfer of Samples in Policy Search via Multiple Importance Sampling Andrea Tirinzoni, Mattia Salvini, Marcello Restelli link
422 Optimal Transport for structured data with application on graphs Vayer Titouan, Nicolas Courty, Romain Tavenard, Chapel Laetitia, R?mi Flamary link
423 Discovering Latent Covariance Structures for Multiple Time Series Anh Tong, Jaesik Choi link
424 Bayesian Generative Active Deep Learning Toan Tran, Thanh-Toan Do, Ian Reid, Gustavo Carneiro link
425 LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations Brian Trippe, Jonathan Huggins, Raj Agrawal, Tamara Broderick link
426 Learning Hawkes Processes Under Synchronization Noise William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran link
427 Homomorphic Sensing Manolis Tsakiris, Liangzu Peng link
428 Metropolis-Hastings Generative Adversarial Networks Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski link
429 Distributed, Egocentric Representations of Graphs for Detecting Critical Structures Ruo-Chun Tzeng, Shan-Hung Wu link
430 Fairness without Harm: Decoupled Classifiers with Preference Guarantees Berk Ustun, Yang Liu, David Parkes link
431 Large-Scale Sparse Kernel Canonical Correlation Analysis Viivi Uurtio, Sahely Bhadra, Juho Rousu link
432 Composing Value Functions in Reinforcement Learning Benjamin Van Niekerk, Steven James, Adam Earle, Benjamin Rosman link
433 Model Comparison for Semantic Grouping Francisco Vargas, Kamen Brestnichki, Nils Hammerla link
434 Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh link
435 Manifold Mixup: Better Representations by Interpolating Hidden States Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio link
436 Maximum Likelihood Estimation for Learning Populations of Parameters Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham Kakade link
437 Learning to select for a predefined ranking Aleksandr Vorobev, Aleksei Ustimenko, Gleb Gusev, Pavel Serdyukov link
438 Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute Tong Wang link
439 Convolutional Poisson Gamma Belief Network Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou link
440 Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori, Yiannis Demiris link
441 SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver Po-Wei Wang, Priya Donti, Bryan Wilder, Zico Kolter link
442 Improving Neural Language Modeling via Adversarial Training Dilin Wang, Chengyue Gong, Qiang Liu link
443 EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis Chaoqi Wang, Roger Grosse, Sanja Fidler, Guodong Zhang link
444 Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models Dilin Wang, Qiang Liu link
445 On the Convergence and Robustness of Adversarial Training Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu link
446 State-Regularized Recurrent Neural Networks Cheng Wang, Mathias Niepert link
447 Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions Hao Wang, Berk Ustun, Flavio Calmon link
448 On Sparse Linear Regression in the Local Differential Privacy Model Di Wang, Jinhui Xu link
449 AdaGrad stepsizes: sharp convergence over nonconvex landscapes Rachel Ward, Xiaoxia Wu, Leon Bottou link
450 Non-Monotonic Sequential Text Generation Sean Welleck, Kiant? Brantley, Hal Daum? Iii, Kyunghyun Cho link
451 PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach Lily Weng, Pin-Yu Chen, Lam Nguyen, Mark Squillante, Akhilan Boopathy, Ivan Oseledets, Luca Daniel link
452 Learning deep kernels for exponential family densities Li Wenliang, Dougal Sutherland, Heiko Strathmann, Arthur Gretton link
453 Improving Model Selection by Employing the Test Data Max Westphal, Werner Brannath link
454 Moment-Based Variational Inference for Markov Jump Processes Christian Wildner, Heinz Koeppl link
455 End-to-End Probabilistic Inference for Nonstationary Audio Analysis William Wilkinson, Michael Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin link
456 Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen link
457 Wasserstein Adversarial Examples via Projected Sinkhorn Iterations Eric Wong, Frank Schmidt, Zico Kolter link
458 Imitation Learning from Imperfect Demonstration Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama link
459 Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar link
460 Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin Xi-Zhu Wu, Song Liu, Zhi-Hua Zhou link
461 Deep Compressed Sensing Yan Wu, Mihaela Rosca, Timothy Lillicrap link
462 Simplifying Graph Convolutional Networks Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinberger link
463 Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance Cong Xie, Sanmi Koyejo, Indranil Gupta link
464 Differentiable Linearized ADMM Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Lin link
465 Calibrated Approximate Bayesian Inference Hanwen Xing, Geoff Nicholls, Jeong Lee link
466 Power k-Means Clustering Jason Xu, Kenneth Lange link
467 Gromov-Wasserstein Learning for Graph Matching and Node Embedding Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin Duke link
468 Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang link
469 Variational Russian Roulette for Deep Bayesian Nonparametrics Kai Xu, Akash Srivastava, Charles Sutton link
470 Supervised Hierarchical Clustering with Exponential Linkage Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew Mccallum link
471 Learning to Prove Theorems via Interacting with Proof Assistants Kaiyu Yang, Jia Deng link
472 LegoNet: Efficient Convolutional Neural Networks with Lego Filters Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu link
473 SWALP : Stochastic Weight Averaging in Low Precision Training Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Chris De Sa link
474 ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation Yuzhe Yang, Guo Zhang, Zhi Xu, Dina Katabi link
475 Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations Quanming Yao, James Tin-Yau Kwok, Bo Han link
476 Hierarchically Structured Meta-learning Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li link
477 Rademacher Complexity for Adversarially Robust Generalization Dong Yin, Ramchandran Kannan, Peter Bartlett link
478 ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables Mingzhang Yin, Yuguang Yue, Mingyuan Zhou link
479 NAS-Bench-101: Towards Reproducible Neural Architecture Search Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter link
480 TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning Sung Whan Yoon, Jun Seo, Jaekyun Moon link
481 Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation Kaichao You, Ximei Wang, Mingsheng Long, Michael Jordan link
482 Position-aware Graph Neural Networks Jiaxuan You, Rex Ying, Jure Leskovec link
483 DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu, Jie Chen, Tian Gao, Mo Yu link
484 Multi-Agent Adversarial Inverse Reinforcement Learning Lantao Yu, Jiaming Song, Stefano Ermon link
485 Online Adaptive Principal Component Analysis and Its extensions Jianjun Yuan, Andrew Lamperski link
486 Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation Jinyang Yuan, Bin Li, Xiangyang Xue link
487 Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning Jihun Yun, Peng Zheng, Eunho Yang, Aurelie Lozano, Aleksandr Aravkin link
488 Bayesian Nonparametric Federated Learning of Neural Networks Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang, Yasaman Khazaeni link
489 Dirichlet Simplex Nest and Geometric Inference Mikhail Yurochkin, Aritra Guha, Yuekai Sun, Xuanlong Nguyen link
490 Global Convergence of Block Coordinate Descent in Deep Learning Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao link
491 Making Convolutional Networks Shift-Invariant Again Richard Zhang link
492 Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback Chicheng Zhang, Alekh Agarwal, Hal Daum? Iii, John Langford, Sahand Negahban link
493 Self-Attention Generative Adversarial Networks Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena link
494 Circuit-GNN: Graph Neural Networks for Distributed Circuit Design Guo Zhang, Hao He, Dina Katabi link
495 LatentGNN: Learning Efficient Non-local Relations for Visual Recognition Songyang Zhang, Xuming He, Shipeng Yan link
496 Bridging Theory and Algorithm for Domain Adaptation Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael Jordan link
497 Random Function Priors for Correlation Modeling Aonan Zhang, John Paisley link
498 Co-Representation Network for Generalized Zero-Shot Learning Fei Zhang, Guangming Shi link
499 SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levine link
500 Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models Chenyang Zhang, Guosheng Yin link
501 Theoretically Principled Trade-off between Robustness and Accuracy Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, Michael Jordan link
502 Learning Novel Policies For Tasks Yunbo Zhang, Wenhao Yu, Greg Turk link
503 Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization Kai Zhang, Sheng Zhang, Jun Liu, Jun Wang, Jie Zhang link
504 Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang, Zhanxing Zhu link
505 Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits Martin Zhang, James Zou, David Tse link
506 On Learning Invariant Representations for Domain Adaptation Han Zhao, Remi Tachet Des Combes, Kun Zhang, Geoffrey Gordon link
507 Metric-Optimized Example Weights Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta link
508 Improving Neural Network Quantization without Retraining using Outlier Channel Splitting Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, Zhiru Zhang link
509 Maximum Entropy-Regularized Multi-Goal Reinforcement Learning Rui Zhao, Xudong Sun, Volker Tresp link
510 Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization Baojian Zhou, Feng Chen, Yiming Ying link
511 Lipschitz Generative Adversarial Nets Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang link
512 BayesNAS: A Bayesian Approach for Neural Architecture Search Hongpeng Zhou, Minghao Yang, Jun Wang, Wei Pan link
513 Transferable Clean-Label Poisoning Attacks on Deep Neural Nets Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein link
514 Improved Dynamic Graph Learning through Fault-Tolerant Sparsification Chunjiang Zhu, Sabine Storandt, Kam-Yiu Lam, Song Han, Jinbo Bi link
515 Poission Subsampled R?nyi Differential Privacy Yuqing Zhu, Yu-Xiang Wang link
516 The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma link
517 Latent Normalizing Flows for Discrete Sequences Zachary Ziegler, Alexander Rush link
518 Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously Julian Zimmert, Haipeng Luo, Chen-Yu Wei link
519 Fast Context Adaptation via Meta-Learning Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, Shimon Whiteson link