AISTATS 2019
360 papers
$β^3$-IRT: A New Item Response Model and Its Applications
Yu Chen, Telmo Silva Filho, Ricardo B. Prudencio, Tom Diethe, Peter Flach A Higher-Order Kolmogorov-Smirnov Test
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani A Swiss Army Infinitesimal Jackknife
Ryan Giordano, William Stephenson, Runjing Liu, Michael Jordan, Tamara Broderick Accelerating Imitation Learning with Predictive Models
Ching-An Cheng, Xinyan Yan, Evangelos Theodorou, Byron Boots Adaptive Gaussian Copula ABC
Yanzhi Chen, Michael U. Gutmann Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin Auto-Encoding Total Correlation Explanation
Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan Autoencoding Any Data Through Kernel Autoencoders
Pierre Laforgue, Stéphan Clémençon, Florence d’Alche-Buc Bandit Online Learning with Unknown Delays
Bingcong Li, Tianyi Chen, Georgios B. Giannakis Bernoulli Race Particle Filters
Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis Binary Space Partitioning Forest
Xuhui Fan, Bin Li, Scott SIsson Black Box Quantiles for Kernel Learning
Anthony Tompkins, Ransalu Senanayake, Philippe Morere, Fabio Ramos Block Stability for MAP Inference
Hunter Lang, David Sontag, Aravindan Vijayaraghavan Calibrating Deep Convolutional Gaussian Processes
Gia-Lac Tran, Edwin V. Bonilla, John Cunningham, Pietro Michiardi, Maurizio Filippone Causal Discovery in the Presence of Missing Data
Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba Ba Computation Efficient Coded Linear Transform
Sinong Wang, Jiashang Liu, Ness Shroff, Pengyu Yang Conservative Exploration Using Interleaving
Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru Consistent Online Optimization: Convex and Submodular
Mohammad Reza Karimi Jaghargh, Andreas Krause, Silvio Lattanzi, Sergei Vassilvtiskii Convergence of Gradient Descent on Separable Data
Mor Shpigel Nacson, Jason Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry Correspondence Analysis Using Neural Networks
Hsiang Hsu, Salman Salamatian, Flavio P. Calmon Cost Aware Inference for IoT Devices
Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama Data-Driven Approach to Multiple-Source Domain Adaptation
Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang Database Alignment with Gaussian Features
Osman E. Dai, Daniel Cullina, Negar Kiyavash Deep Learning with Differential Gaussian Process Flows
Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski Deep Topic Models for Multi-Label Learning
Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou, Piyush Rai Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter Bartlett, Martin Wainwright Direct Acceleration of SAGA Using Sampled Negative Momentum
Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo Distilling Policy Distillation
Wojciech M. Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant Jayakumar, Grzegorz Swirszcz, Max Jaderberg Distributed Inexact Newton-Type Pursuit for Non-Convex Sparse Learning
Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Junzhou Huang, Dimitris N. Metaxas Distributional Reinforcement Learning with Linear Function Approximation
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra Domain-Size Aware Markov Logic Networks
Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla Doubly Semi-Implicit Variational Inference
Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov Efficient Greedy Coordinate Descent for Composite Problems
Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi Efficient Inference in Multi-Task Cox Process Models
Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla Efficient Linear Bandits Through Matrix Sketching
Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi Evaluating Model Calibration in Classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas Schön Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models
Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S.V.N. Vishwanathan, Inderjit Dhillon Fast Algorithms for Sparse Reduced-Rank Regression
Benjamin Dubois, Jean-François Delmas, Guillaume Obozinski Fast and Robust Shortest Paths on Manifolds Learned from Data
Georgios Arvanitidis, Soren Hauberg, Philipp Hennig, Michael Schober Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, James Stokes Forward Amortized Inference for Likelihood-Free Variational Marginalization
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva Borne, Yaǧmur Güçlütürk, Max Hinne, Eric Maris, Marcel Gerven From Cost-Sensitive Classification to Tight F-Measure Bounds
Kevin Bascol, Rémi Emonet, Elisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban Hierarchical Clustering for Euclidean Data
Moses Charikar, Vaggos Chatziafratis, Rad Niazadeh, Grigory Yaroslavtsev Imitation-Regularized Offline Learning
Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy Implicit Kernel Learning
Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabas Poczos Improved Semi-Supervised Learning with Multiple Graphs
Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson, Percy Liang Infinite Task Learning in RKHSs
Romain Brault, Alex Lambert, Zoltan Szabo, Maxime Sangnier, Florence d’Alche-Buc Interpolating Between Optimal Transport and MMD Using Sinkhorn Divergences
Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouve, Gabriel Peyré Interpretable Almost-Exact Matching for Causal Inference
Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky Interpretable Cascade Classifiers with Abstention
Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar Iterative Bayesian Learning for Crowdsourced Regression
Jungseul Ok, Sewoong Oh, Yunhun Jang, Jinwoo Shin, Yung Yi KAMA-NNs: Low-Dimensional Rotation Based Neural Networks
Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang Kernel Exponential Family Estimation via Doubly Dual Embedding
Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy
Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Salman A. Avestimehr Large-Margin Classification in Hyperbolic Space
Hyunghoon Cho, Benjamin DeMeo, Jian Peng, Bonnie Berger Learning Controllable Fair Representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon Learning Natural Programs from a Few Examples in Real-Time
Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani Learning Rules-First Classifiers
Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan Learning to Optimize Under Non-Stationarity
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu Learning Tree Structures from Noisy Data
Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate Local Saddle Point Optimization: A Curvature Exploitation Approach
Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann Lovasz Convolutional Networks
Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar Matroids, Matchings, and Fairness
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvtiskii MaxHedge: Maximizing a Maximum Online
Stephen Pasteris, Fabio Vitale, Kevin Chan, Shiqiang Wang, Mark Herbster Minimum Volume Topic Modeling
Byoungwook Jang, Alfred Hero Modularity-Based Sparse Soft Graph Clustering
Alexandre Hollocou, Thomas Bonald, Marc Lelarge Multi-Observation Regression
Rafael Frongillo, Nishant A. Mehta, Tom Morgan, Bo Waggoner Multitask Metric Learning: Theory and Algorithm
Boyu Wang, Hejia Zhang, Peng Liu, Zebang Shen, Joelle Pineau Negative Momentum for Improved Game Dynamics
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas Nonlinear Acceleration of Primal-Dual Algorithms
Raghu Bollapragada, Damien Scieur, Alexandre d’Aspremont On Structure Priors for Learning Bayesian Networks
Ralf Eggeling, Jussi Viinikka, Aleksis Vuoksenmaa, Mikko Koivisto On Target Shift in Adversarial Domain Adaptation
Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David Carlson On Theory for BART
Veronika Ročková, Enakshi Saha Online Algorithm for Unsupervised Sensor Selection
Arun Verma, Manjesh Hanawal, Csaba Szepesvari, Venkatesh Saligrama Optimizing over a Restricted Policy Class in MDPs
Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis Orthogonal Estimation of Wasserstein Distances
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller Overcomplete Independent Component Analysis via SDP
Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis Bach, Alexandre d’Aspremont, David Sontag Performance Metric Elicitation from Pairwise Classifier Comparisons
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo Probabilistic Forecasting with Spline Quantile Function RNNs
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski Proximal Splitting Meets Variance Reduction
Fabian Pedregosa, Kilian Fatras, Mattia Casotto Region-Based Active Learning
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang Regularized Contextual Bandits
Xavier Fontaine, Quentin Berthet, Vianney Perchet Reparameterizing Distributions on Lie Groups
Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré Restarting Frank-Wolfe
Thomas Kerdreux, Alexandre d’Aspremont, Sebastian Pokutta Reversible Jump Probabilistic Programming
David A. Roberts, Marcus Gallagher, Thomas Taimre Revisiting Adversarial Risk
Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar Risk-Sensitive Generative Adversarial Imitation Learning
Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, Marco Pavone Rotting Bandits Are No Harder than Stochastic Ones
Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko Safe Convex Learning Under Uncertain Constraints
Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour Sample Complexity of Sinkhorn Divergences
Aude Genevay, Lénaïc Chizat, Francis Bach, Marco Cuturi, Gabriel Peyré Sample Efficient Graph-Based Optimization with Noisy Observations
Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton Scalable Thompson Sampling via Optimal Transport
Ruiyi Zhang, Zheng Wen, Changyou Chen, Chen Fang, Tong Yu, Lawrence Carin Size of Interventional Markov Equivalence Classes in Random DAG Models
Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler Sketching for Latent Dirichlet-Categorical Models
Joseph Tassarotti, Jean-Baptiste Tristan, Michael Wick SMOGS: Social Network Metrics of Game Success
Fan Bu, Sonia Xu, Katherine Heller, Alexander Volfovsky Sobolev Descent
Youssef Mroueh, Tom Sercu, Anant Raj Sparse Multivariate Bernoulli Processes in High Dimensions
Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash Amini, Sundeep Rangan, Alyson K. Fletcher Statistical Optimal Transport via Factored Couplings
Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Philippe Rigollet, Geoffrey Schiebinger, Jonathan Weed Stochastic Algorithms with Descent Guarantees for ICA
Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach Stochastic Negative Mining for Learning with Large Output Spaces
Sashank J. Reddi, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Jiecao Chen, Sanjiv Kumar Structured Disentangled Representations
Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem Meent Structured Neural Topic Models for Reviews
Babak Esmaeili, Hongyi Huang, Byron Wallace, Jan-Willem van de Meent Structured Robust Submodular Maximization: Offline and Online Algorithms
Nima Anari, Nika Haghtalab, Seffi Naor, Sebastian Pokutta, Mohit Singh, Alfredo Torrico Temporal Quilting for Survival Analysis
Changhee Lee, William Zame, Ahmed Alaa, Mihaela Schaar The Gaussian Process Autoregressive Regression Model (GPAR)
James Requeima, William Tebbutt, Wessel Bruinsma, Richard E. Turner The Termination Critic
Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup Top Feasible Arm Identification
Julian Katz-Samuels, Clayton Scott Towards Efficient Data Valuation Based on the Shapley Value
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, Costas J. Spanos Towards Optimal Transport with Global Invariances
David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola Truncated Back-Propagation for Bilevel Optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots Unbiased Smoothing Using Particle Independent Metropolis-Hastings
Lawrece Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob