AISTATS 2019

360 papers

Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization

Aditya Grover, Stefano Ermon

An Online Algorithm for Smoothed Regression and LQR Control

Gautam Goel, Adam Wierman

An Optimal Control Approach to Sequential Machine Teaching

Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu

Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods

Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie

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

HS^2: Active learning over hypergraphs with pointwise and pairwise queries

I (Eli) Chien, Huozhi Zhou, Pan Li

Statistical Optimal Transport via Factored Couplings

Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Philippe Rigollet, Geoffrey Schiebinger

Interpretable Almost-Exact Matching for Causal Inference

Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Online learning with feedback graphs and switching costs

Anshuka Rangi, Massimo Franceschetti

Modeling simple structures and geometry for better stochastic optimization algorithms

Hilal Asi, John C. Duchi

Fast Algorithms for Sparse Reduced-Rank Regression

Benjamin Dubois, Jean-François Delmas, Guillaume Obozinski

SMOGS: Social Network Metrics of Game Success

Fan Bu, Sonia Xu, Katherine Heller, Alexander Volfovsky

Avoiding Latent Variable Collapse with Generative Skip Models

Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei

Two-temperature logistic regression based on the Tsallis divergence

Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan

Unbiased Smoothing using Particle Independent Metropolis-Hastings

Lawrece Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

Anton Mallasto, Søren Hauberg, Aasa Feragen

Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models

Kaspar Märtens, Michalis Titsias, Christopher Yau

Bernoulli Race Particle Filters

Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis

A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems

Rishabh Iyer, Jeffrey Bilmes

Revisiting Adversarial Risk

Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar

Kernel Exponential Family Estimation via Doubly Dual Embedding

Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans

Interpretable Cascade Classifiers with Abstention

Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar

Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series

Rui Xie, Zengyan Wang, Shuyang Bai, Ping Ma, Wenxuan Zhong

Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes

Linfeng Liu, Liping Liu

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding

Nathan Kallus, Xiaojie Mao, Angela Zhou

Least Squares Estimation of Weakly Convex Functions

Sun Sun, Yaoliang Yu

Probabilistic Semantic Inpainting with Pixel Constrained CNNs

Emilien Dupont, Suhas Suresha

Learning Determinantal Point Processes by Corrective Negative Sampling

Zelda Mariet, Mike Gartrell, Suvrit Sra

Consistent Online Optimization: Convex and Submodular

Mohammad Reza Karimi Jaghargh, Andreas Krause, Silvio Lattanzi, Sergei Vassilvtiskii

The Termination Critic

Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos

Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function

Wojciech Tarnowski, Piotr Warchoł, Stanisław Jastrzebski, Jacek Tabor, Maciej Nowak

Matroids, Matchings, and Fairness

Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvtiskii

Distributional reinforcement learning with linear function approximation

Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

Arno Solin, Manon Kok

Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization

Jaime Roquero Gimenez, James Zou

Multi-Task Time Series Analysis applied to Drug Response Modelling

Alex Bird, Chris Williams, Christopher Hawthorne

Learning Controllable Fair Representations

Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon

Risk-Sensitive Generative Adversarial Imitation Learning

Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, Marco Pavone

Regularized Contextual Bandits

Xavier Fontaine, Quentin Berthet, Vianney Perchet

Training Variational Autoencoders with Buffered Stochastic Variational Inference

Rui Shu, Hung Bui, Jay Whang, Stefano Ermon

Knockoffs for the Mass: New Feature Importance Statistics with False Discovery Guarantees

Jaime Roquero Gimenez, Amirata Ghorbani, James Zou

The non-parametric bootstrap and spectral analysis in moderate and high-dimension

Noureddine El Karoui, Elizabeth Purdom

Safe Convex Learning under Uncertain Constraints

Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour

Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

On Euclidean k-Means Clustering with alpha-Center Proximity

Amit Deshpande, Anand Louis, Apoorv Singh

Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets

Mehrdad Ghadiri, Mark Schmidt

Test without Trust: Optimal Locally Private Distribution Testing

Jayadev Acharya, Clement Canonne, Cody Freitag, Himanshu Tyagi

Provable Robustness of ReLU networks via Maximization of Linear Regions

Francesco Croce, Maksym Andriushchenko, Matthias Hein

Projection-Free Bandit Convex Optimization

Lin Chen, Mingrui Zhang, Amin Karbasi

Renyi Differentially Private ERM for Smooth Objectives

Chen Chen, Jaewoo Lee, Dan Kifer

Variational Information Planning for Sequential Decision Making

Jason Pacheco, John Fisher

Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature

Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause

Implicit Kernel Learning

Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos

Gaussian Process Modulated Cox Processes under Linear Inequality Constraints

Andrés F. Lopez-Lopera, ST John, Nicolas Durrande

Bridging the gap between regret minimization and best arm identification, with application to A/B tests

Rémy Degenne, Thomas Nedelec, Clément Calauzènes, Vianney Perchet

Lovasz Convolutional Networks

Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar

Non-linear process convolutions for multi-output Gaussian processes

Mauricio A. Álvarez, Wil Ward, Cristian Guarnizo

A Geometric Perspective on the Transferability of Adversarial Directions

Zachary Charles, Harrison Rosenberg, Dimitris Papailiopoulos

Learning One-hidden-layer Neural Networks under General Input Distributions

Weihao Gao, Ashok V. Makkuva, Sewoong Oh, Pramod Viswanath

Stochastic Negative Mining for Learning with Large Output Spaces

Sashank J. Reddi, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Jiecao Chen

Designing Optimal Binary Rating Systems

Nikhil Garg, Ramesh Johari

Sharp Analysis of Learning with Discrete Losses

Alex Nowak, Francis Bach, Alessandro Rudi

Exponential Weights on the Hypercube in Polynomial Time

Sudeep Raja Putta, Abhishek Shetty

Probabilistic Forecasting with Spline Quantile Function RNNs

Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas

Sequential Patient Recruitment and Allocation for Adaptive Clinical Trials

Onur Atan, William R. Zame, Mihaela Schaar

Unsupervised Alignment of Embeddings with Wasserstein Procrustes

Edouard Grave, Armand Joulin, Quentin Berthet

Towards Optimal Transport with Global Invariances

David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola

The Gaussian Process Autoregressive Regression Model (GPAR)

James Requeima, William Tebbutt, Wessel Bruinsma, Richard E. Turner

MaxHedge: Maximizing a Maximum Online

Stephen Pasteris, Fabio Vitale, Kevin Chan, Shiqiang Wang, Mark Herbster

Generalizing the theory of cooperative inference

Pei Wang, Pushpi Paranamana, Patrick Shafto

Large-Margin Classification in Hyperbolic Space

Hyunghoon Cho, Benjamin DeMeo, Jian Peng, Bonnie Berger

Data-dependent compression of random features for large-scale kernel approximation

Raj Agrawal, Trevor Campbell, Jonathan Huggins, Tamara Broderick

Deep learning with differential Gaussian process flows

Pashupati Hegde, Markus Heinonen, Harri Lahdesmaki, Samuel Kaski

Negative Momentum for Improved Game Dynamics

Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar

Active multiple matrix completion with adaptive confidence sets

Andrea Locatelli, Alexandra Carpentier, Michal Valko

Learning Tree Structures from Noisy Data

Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate

Causal Discovery in the Presence of Missing Data

Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström

Lifted Weight Learning of Markov Logic Networks Revisited

Ondrej Kuzelka, Vyacheslav Kungurtsev

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data

Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

Truncated Back-propagation for Bilevel Optimization

Amirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots

Learning Natural Programs from a Few Examples in Real-Time

Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani

Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring

Alexander F. Lapanowski, Irina Gaynanova

Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs

Alexander Bauer, Shinichi Nakajima, Nico Goernitz, Klaus-Robert Müller

On Structure Priors for Learning Bayesian Networks

Ralf Eggeling, Jussi Viinikka, Aleksis Vuoksenmaa, Mikko Koivisto

Statistical Learning under Nonstationary Mixing Processes

Steve Hanneke, Liu Yang

Finding the bandit in a graph: Sequential search-and-stop

Pierre Perrault, Vianney Perchet, Michal Valko

Semi-supervised clustering for de-duplication

Shrinu Kushagra, Shai Ben-David, Ihab Ilyas

Differentially Private Online Submodular Minimization

Adrian Rivera Cardoso, Rachel Cummings

A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions

Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka

Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning

Guillaume Rabusseau, Tianyu Li, Doina Precup

Inverting Supervised Representations with Autoregressive Neural Density Models

Charlie Nash, Nate Kushman, Christopher K.I. Williams

Does data interpolation contradict statistical optimality?

Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov

Direct Acceleration of SAGA using Sampled Negative Momentum

Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li

Top Feasible Arm Identification

Julian Katz-Samuels, Clayton Scott

Adaptive Gaussian Copula ABC

Yanzhi Chen, Michael U. Gutmann

Sample Complexity of Sinkhorn Divergences

Aude Genevay, Lénaïc Chizat, Francis Bach, Marco Cuturi, Gabriel Peyré

Stochastic algorithms with descent guarantees for ICA

Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach

Calibrating Deep Convolutional Gaussian Processes

Gia-Lac Tran, Edwin V. Bonilla, John Cunningham, Pietro Michiardi, Maurizio Filippone