AISTATS 2020
423 papers
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
Nhan Pham, Lam Nguyen, Dzung Phan, Phuong Ha Nguyen, Marten Dijk, Quoc Tran-Dinh A Locally Adaptive Bayesian Cubature Method
Matthew Fisher, Chris Oates, Catherine Powell, Aretha Teckentrup A Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, Joao Carvalho, Hany Abdulsamad, Jan Peters A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
Stefan Haller, Mangal Prakash, Lisa Hutschenreiter, Tobias Pietzsch, Carsten Rother, Florian Jug, Paul Swoboda, Bogdan Savchynskyy A Robust Univariate Mean Estimator Is All You Need
Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar A Single Algorithm for Both Restless and Rested Rotting Bandits
Julien Seznec, Pierre Menard, Alessandro Lazaric, Michal Valko A Topology Layer for Machine Learning
Rickard Brüel Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath, Primoz Skraba Accelerated Bayesian Optimisation Through Weight-Prior Tuning
Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Svetha Venkatesh, Laurence Park, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak Accelerating Gradient Boosting Machines
Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni Accelerating Smooth Games by Manipulating Spectral Shapes
Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel Adversarial Robustness Guarantees for Classification with Gaussian Processes
Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska, Stephen Roberts Almost-Matching-Exactly for Treatment Effect Estimation Under Network Interference
Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky ASAP: Architecture Search, Anneal and Prune
Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik Assessing Local Generalization Capability in Deep Models
Huan Wang, Nitish Shirish Keskar, Caiming Xiong, Richard Socher Asynchronous Gibbs Sampling
Alexander Terenin, Daniel Simpson, David Draper Automatic Differentiation of Sketched Regression
Hang Liao, Barak A. Pearlmutter, Vamsi K. Potluru, David P. Woodruff Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection
Vaggos Chatziafratis, Grigory Yaroslavtsev, Euiwoong Lee, Konstantin Makarychev, Sara Ahmadian, Alessandro Epasto, Mohammad Mahdian Black-Box Inference for Non-Linear Latent Force Models
Wil Ward, Tom Ryder, Dennis Prangle, Mauricio Alvarez Budget Learning via Bracketing
Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama Causal Bayesian Optimization
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González Characterization of Overlap in Observational Studies
Michael Oberst, Fredrik Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush Varshney ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing Competing Bandits in Matching Markets
Lydia T. Liu, Horia Mania, Michael Jordan Conditional Importance Sampling for Off-Policy Learning
Mark Rowland, Anna Harutyunyan, Hado Hasselt, Diana Borsa, Tom Schaul, Remi Munos, Will Dabney Conditional Linear Regression
Diego Calderon, Brendan Juba, Sirui Li, Zongyi Li, Lisa Ruan Conservative Exploration in Reinforcement Learning
Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta Coping with Simulators That Don’t Always Return
Andrew Warrington, Saeid Naderiparizi, Frank Wood Data Generation for Neural Programming by Example
Judith Clymo, Haik Manukian, Nathanael Fijalkow, Adria Gascon, Brooks Paige DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gurbuzbalaban Decentralized Gradient Methods: Does Topology Matter?
Giovanni Neglia, Chuan Xu, Don Towsley, Gianmarco Calbi Deep Structured Mixtures of Gaussian Processes
Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen Derivative-Free & Order-Robust Optimisation
Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko Differentiable Causal Backdoor Discovery
Limor Gultchin, Matt Kusner, Varun Kanade, Ricardo Silva Distributed, Partially Collapsed MCMC for Bayesian Nonparametrics
Kumar Avinava Dubey, Michael Zhang, Eric Xing, Sinead Williamson Distributionally Robust Bayesian Optimization
Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause Distributionally Robust Bayesian Quadrature Optimization
Thanh Nguyen, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh Doubly Sparse Variational Gaussian Processes
Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman Dynamic Content Based Ranking
Seppo Virtanen, Mark Girolami DYNOTEARS: Structure Learning from Time-Series Data
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam Efficient Spectrum-Revealing CUR Matrix Decomposition
Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu Entropy Weighted Power K-Means Clustering
Saptarshi Chakraborty, Debolina Paul, Swagatam Das, Jason Xu Expressiveness and Learning of Hidden Quantum Markov Models
Sandesh Adhikary, Siddarth Srinivasan, Geoff Gordon, Byron Boots Fair Correlation Clustering
Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian Fair Decisions Despite Imperfect Predictions
Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera Fairness Evaluation in Presence of Biased Noisy Labels
Riccardo Fogliato, Alexandra Chouldechova, Max G’Sell Fast and Accurate Ranking Regression
Ilkay Yildiz, Jennifer Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis Fast and Bayes-Consistent Nearest Neighbors
Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt Fast and Furious Convergence: Stochastic Second Order Methods Under Interpolation
Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji), Mark Schmidt, Simon Lacoste-Julien Fast Noise Removal for K-Means Clustering
Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou Federated Heavy Hitters Discovery with Differential Privacy
Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
Xuedong Shang, Rianne Heide, Pierre Menard, Emilie Kaufmann, Michal Valko Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric Gain with No Pain: Efficiency of Kernel-PCA by Nyström Sampling
Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi GAIT: A Geometric Approach to Information Theory
Jose Gallego Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien Gaussianization Flows
Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon GP-VAE: Deep Probabilistic Time Series Imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Raetsch, Stephan Mandt Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth Hamiltonian Monte Carlo Swindles
Dan Piponi, Matthew Hoffman, Pavel Sountsov High Dimensional Robust Sparse Regression
Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis How to Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov Hyperbolic Manifold Regression
Gian Marconi, Carlo Ciliberto, Lorenzo Rosasco Hypothesis Testing Interpretations and Renyi Differential Privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato Inference of Dynamic Graph Changes for Functional Connectome
Dingjue Ji, Junwei Lu, Yiliang Zhang, Siyuan Gao, Hongyu Zhao Interpretable Deep Gaussian Processes with Moments
Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao, Patrick Shafto Invertible Generative Modeling Using Linear Rational Splines
Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie Ivy: Instrumental Variable Synthesis for Causal Inference
Zhaobin Kuang, Frederic Sala, Nimit Sohoni, Sen Wu, Aldo Córdova-Palomera, Jared Dunnmon, James Priest, Christopher Re Kernel Conditional Density Operators
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet Langevin Monte Carlo Without Smoothness
Niladri Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter Bartlett Learnable Bernoulli Dropout for Bayesian Deep Learning
Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh, Mingyuan Zhou, Xiaoning Qian Learning Fair Representations for Kernel Models
Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar Learning in Gated Neural Networks
Ashok Makkuva, Sewoong Oh, Sreeram Kannan, Pramod Viswanath Learning Sparse Nonparametric DAGs
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric Xing Learning with Minibatch Wasserstein : Asymptotic and Gradient Properties
Kilian Fatras, Younes Zine, Rémi Flamary, Remi Gribonval, Nicolas Courty LIBRE: Learning Interpretable Boolean Rule Ensembles
Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi Linearly Convergent Frank-Wolfe with Backtracking Line-Search
Fabian Pedregosa, Geoffrey Negiar, Armin Askari, Martin Jaggi Local Differential Privacy for Sampling
Hisham Husain, Borja Balle, Zac Cranko, Richard Nock Locally Accelerated Conditional Gradients
Jelena Diakonikolas, Alejandro Carderera, Sebastian Pokutta Minimax Rank-$1$ Matrix Factorization
Venkatesh Saligrama, Alexander Olshevsky, Julien Hendrickx Mixed Strategies for Robust Optimization of Unknown Objectives
Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause Momentum in Reinforcement Learning
Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist Monotonic Gaussian Process Flows
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell More Powerful Selective Kernel Tests for Feature Selection
Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira Naive Feature Selection: Sparsity in Naive Bayes
Armin Askari, Alexandre d’Aspremont, Laurent El Ghaoui Nested-Wasserstein Self-Imitation Learning for Sequence Generation
Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen, Wenlin Wang, Lawrence Carin Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
Lukas Fröhlich, Edgar Klenske, Julia Vinogradska, Christian Daniel, Melanie Zeilinger Non-Parametric Calibration for Classification
Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel) Nonmyopic Gaussian Process Optimization with Macro-Actions
Dmitrii Kharkovskii, Chun Kai Ling, Bryan Kian Hsiang Low Nonparametric Estimation in the Dynamic Bradley-Terry Model
Heejong Bong, Wanshan Li, Shamindra Shrotriya, Alessandro Rinaldo Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton On Minimax Optimality of GANs for Robust Mean Estimation
Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu On Pruning for Score-Based Bayesian Network Structure Learning
Alvaro Henrique Chaim Correia, James Cussens, Cassio de Campos On the Convergence of SARAH and Beyond
Bingcong Li, Meng Ma, Georgios B. Giannakis On the Interplay Between Noise and Curvature and Its Effect on Optimization and Generalization
Valentin Thomas, Fabian Pedregosa, Bart Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux One Sample Stochastic Frank-Wolfe
Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi Optimal Algorithms for Multiplayer Multi-Armed Bandits
Po-An Wang, Alexandre Proutiere, Kaito Ariu, Yassir Jedra, Alessio Russo Optimal Sampling in Unbiased Active Learning
Henrik Imberg, Johan Jonasson, Marina Axelson-Fisk Optimized Score Transformation for Fair Classification
Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio Calmon Orthogonal Gradient Descent for Continual Learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, Ang Li Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang, Corey Chivers, Michael Draugelis, Kai Li, Barbara Engelhardt Permutation Invariant Graph Generation via Score-Based Generative Modeling
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon Precision-Recall Curves Using Information Divergence Frontiers
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly Prophets, Secretaries, and Maximizing the Probability of Choosing the Best
Hossein Esfandiari, MohammadTaghi Hajiaghayi, Brendan Lucier, Michael Mitzenmacher Randomized Exploration in Generalized Linear Bandits
Branislav Kveton, Manzil Zaheer, Csaba Szepesvari, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier Rep the Set: Neural Networks for Learning Set Representations
Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, Michalis Vazirgiannis Revisiting Stochastic Extragradient
Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Yura Malitsky Rk-Means: Fast Clustering for Relational Data
Ryan Curtin, Benjamin Moseley, Hung Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich Robust Optimisation Monte Carlo
Borislav Ikonomov, Michael U. Gutmann Robust Stackelberg Buyers in Repeated Auctions
Thomas Nedelec, Clement Calauzenes, Vianney Perchet, Noureddine El Karoui Safe-Bayesian Generalized Linear Regression
Rianne Heide, Alisa Kirichenko, Peter Grunwald, Nishant Mehta Sample Complexity Bounds for Localized Sketching
Rakshith Sharma Srinivasa, Mark Davenport, Justin Romberg Scalable Gradients for Stochastic Differential Equations
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Amir Zandieh, Navid Nouri, Ameya Velingker, Michael Kapralov, Ilya Razenshteyn Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models
Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin Wainwright, Michael Jordan, Bin Yu Stein Variational Inference for Discrete Distributions
Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu Stochastic Neural Network with Kronecker Flow
Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville Tensorized Random Projections
Beheshteh Rakhshan, Guillaume Rabusseau The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits
Ronshee Chawla, Abishek Sankararaman, Ayalvadi Ganesh, Sanjay Shakkottai The Power of Batching in Multiple Hypothesis Testing
Tijana Zrnic, Daniel Jiang, Aaditya Ramdas, Michael Jordan Thresholding Graph Bandits with GrAPL
Daniel LeJeune, Gautam Dasarathy, Richard Baraniuk Towards Competitive N-Gram Smoothing
Moein Falahatgar, Mesrob Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati Truly Batch Model-Free Inverse Reinforcement Learning About Multiple Intentions
Giorgia Ramponi, Amarildo Likmeta, Alberto Maria Metelli, Andrea Tirinzoni, Marcello Restelli Two-Sample Testing Using Deep Learning
Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Zepeng Huo, Arash PakBin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi Unconditional Coresets for Regularized Loss Minimization
Alireza Samadian, Kirk Pruhs, Benjamin Moseley, Sungjin Im, Ryan Curtin Understanding Generalization in Deep Learning via Tensor Methods
Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang Understanding the Effects of Batching in Online Active Learning
Kareem Amin, Corinna Cortes, Giulia DeSalvo, Afshin Rostamizadeh Validated Variational Inference via Practical Posterior Error Bounds
Jonathan Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick Value Preserving State-Action Abstractions
David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael Littman Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, Aapo Hyvarinen Variational Autoencoders for Sparse and Overdispersed Discrete Data
He Zhao, Piyush Rai, Lan Du, Wray Buntine, Dinh Phung, Mingyuan Zhou Variational Integrator Networks for Physically Structured Embeddings
Steindor Saemundsson, Alexander Terenin, Katja Hofmann, Marc Deisenroth