AISTATS 2020

423 papers

A Reduction from Reinforcement Learning to No-Regret Online Learning

Ching-An Cheng, Remi Tachet Combes, Byron Boots, Geoff Gordon

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

Adaptive multi-fidelity optimization with fast learning rates

Côme Fiegel, Victor Gabillon, Michal Valko

Understanding the Effects of Batching in Online Active Learning

Kareem Amin, Corinna Cortes, Giulia DeSalvo, Afshin Rostamizadeh

The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits

Ronshee Chawla, Abishek Sankararaman, Ayalvadi Ganesh, Sanjay Shakkottai

An Inverse-free Truncated Rayleigh-Ritz Method for Sparse Generalized Eigenvalue Problem

Yunfeng Cai, Ping Li

Data Generation for Neural Programming by Example

Judith Clymo, Haik Manukian, Nathanaël Fijalkow, Adria Gascon, Brooks Paige

Context Mover’s Distance & Barycenters: Optimal Transport of Contexts for Building Representations

Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi

Minimax Rank-$1$ Matrix Factorization

Venkatesh Saligrama, Alexander Olshevsky, Julien Hendrickx

Learning Sparse Nonparametric DAGs

Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric Xing

Local Differential Privacy for Sampling

Hisham Husain, Borja Balle, Zac Cranko, Richard Nock

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos

Causal inference in degenerate systems: An impossibility result

Yue Wang, Linbo Wang

Adversarial Robustness Guarantees for Classification with Gaussian Processes

Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska

Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training

Fangda Gu, Armin Askari, Laurent El Ghaoui

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

Niccolò Dalmasso, Ann Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim

Learning in Gated Neural Networks

Ashok Makkuva, Sewoong Oh, Sreeram Kannan, Pramod Viswanath

Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency

Ziv Goldfeld, Kristjan Greenewald

Nonparametric Estimation in the Dynamic Bradley-Terry Model

Heejong Bong, Wanshan Li, Shamindra Shrotriya, Alessandro Rinaldo

Tensorized Random Projections

Beheshteh Rakhshan, Guillaume Rabusseau

Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes

Zhaozhi Qian, Ahmed Alaa, Alexis Bellot, Mihaela Schaar, Jem Rashbass

An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays

Julian Zimmert, Yevgeny Seldin

Sample complexity bounds for localized sketching

Rakshith Sharma Srinivasa, Mark Davenport, Justin Romberg

“Bring Your Own Greedy”+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack

Grigory Yaroslavtsev, Samson Zhou, Dmitrii Avdiukhin

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin

An approximate KLD based experimental design for models with intractable likelihoods

Ziqiao Ao, Jinglai Li

Inference of Dynamic Graph Changes for Functional Connectome

Dingjue Ji, Junwei Lu, Yiliang Zhang, Siyuan Gao, Hongyu Zhao

Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation

Sangdon Park, Osbert Bastani, James Weimer, Insup Lee

Non-exchangeable feature allocation models with sublinear growth of the feature sizes

Giuseppe Di Benedetto, Francois Caron, Yee Whye Teh

Bayesian experimental design using regularized determinantal point processes

Michal Derezinski, Feynman Liang, Michael Mahoney

Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space

Quentin Mérigot, Alex Delalande, Frederic Chazal

A Novel Confidence-Based Algorithm for Structured Bandits

Andrea Tirinzoni, Alessandro Lazaric, Marcello Restelli

Linear predictor on linearly-generated data with missing values: non consistency and solutions

Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gael Varoquaux

Causal Bayesian Optimization

Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier Gonzalez

Enriched mixtures of generalised Gaussian process experts

Charles Gadd, Sara Wade, Alexis Boukouvalas

Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis

Kaige Yang, Laura Toni, Xiaowen Dong

Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection

Vaggos Chatziafratis, Grigory Yaroslavtsev, Euiwoong Lee, Konstantin Makarychev, Sara Ahmadian

Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling

Mojmir Mutny, Michal Derezinski, Andreas Krause

Importance Sampling via Local Sensitivity

Anant Raj, Cameron Musco, Lester Mackey

Black-Box Inference for Non-Linear Latent Force Models

Wil Ward, Tom Ryder, Dennis Prangle, Mauricio Alvarez

Variational Integrator Networks for Physically Structured Embeddings

Steindor Saemundsson, Alexander Terenin, Katja Hofmann, Marc Deisenroth

Flexible distribution-free conditional predictive bands using density estimators

Rafael Izbicki, Gilson Shimizu, Rafael Stern

Monotonic Gaussian Process Flows

Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell

Deterministic Decoding for Discrete Data in Variational Autoencoders

Daniil Polykovskiy, Dmitry Vetrov

Bayesian Reinforcement Learning via Deep, Sparse Sampling

Divya Grover, Debabrota Basu, Christos Dimitrakakis

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models

Théo Galy-Fajou, Florian Wenzel, Manfred Opper

Finite-Time Error Bounds for Biased Stochastic Approximation with Applications to Q-Learning

Gang Wang, Georgios B. Giannakis

Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference

Jonathan Lee, Aldo Pacchiano, Michael Jordan

Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity

Aaron Sidford, Mengdi Wang, Lin Yang, Yinyu Ye

Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees

Atsushi Nitanda, Taiji Suzuki

Mixed Strategies for Robust Optimization of Unknown Objectives

Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments

Adam Foster, Martin Jankowiak, Matthew O’Meara, Yee Whye Teh, Tom Rainforth

Exploiting Categorical Structure Using Tree-Based Methods

Brian Lucena

How To Backdoor Federated Learning

Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov

BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders

Kaspar Märtens, Christopher Yau

Neural Decomposition: Functional ANOVA with Variational Autoencoders

Kaspar Märtens, Christopher Yau

Feature relevance quantification in explainable AI: A causal problem

Dominik Janzing, Lenon Minorics, Patrick Bloebaum

Persistence Enhanced Graph Neural Network

Qi Zhao, Ze Ye, Chao Chen, Yusu Wang

Online Convex Optimization with Perturbed Constraints: Optimal Rates against Stronger Benchmarks

Victor Valls, George Iosifidis, Douglas Leith, Leandros Tassiulas

Doubly Sparse Variational Gaussian Processes

Vincent ADAM, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games

Waïss Azizian, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau

Fast Markov chain Monte Carlo algorithms via Lie groups

Steve Huntsman

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis

Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar

Robust Optimisation Monte Carlo

Borislav Ikonomov, Michael U. Gutmann

Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout

Xubo Yue, Raed AL Kontar

MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search

Insu Han, Jennifer Gillenwater

PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures

Mathieu Carrière, Frederic Chazal, Yuichi Ike, Theo Lacombe, Martin Royer

Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization

Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy

Integrals over Gaussians under Linear Domain Constraints

Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig

Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions

Loucas Pillaud-Vivien, Francis Bach, Tony Lelièvre, Alessandro Rudi, Gabriel Stoltz

Rk-means: Fast Clustering for Relational Data

Ryan Curtin, Benjamin Moseley, Hung Ngo, Xuanlong Nguyen, Dan Olteanu

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization

Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne

Statistical and Computational Rates in Graph Logistic Regression

Quentin Berthet, Nicolai Baldin

On Pruning for Score-Based Bayesian Network Structure Learning

Alvaro Henrique Chaim Correia, James Cussens, Cassio de Campos

Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters

Yuhao Wang, Uma Roy, Caroline Uhler

Statistical guarantees for local graph clustering

Wooseok Ha, Kimon Fountoulakis, Michael Mahoney

The Quantile Snapshot Scan: Comparing Quantiles of Spatial Data from Two Snapshots in Time

Travis Moore, Wong Weng-Keen

Learning Entangled Single-Sample Distributions via Iterative Trimming

Hui Yuan, Yingyu Liang

Support recovery and sup-norm convergence rates for sparse pivotal estimation

Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon

Contextual Constrained Learning for Dose-Finding Clinical Trials

Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela Schaar

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere

Safe-Bayesian Generalized Linear Regression

Rianne Heide, Alisa Kirichenko, Peter Grünwald, Nishant Mehta

On Thompson Sampling for Smoother-than-Lipschitz Bandits

James Grant, David Leslie

GAIT: A Geometric Approach to Information Theory

Jose Gallego Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien

Thresholding Bandit Problem with Both Duels and Pulls

Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski

Approximate Inference with Wasserstein Gradient Flows

Charlie Frogner, Tomaso Poggio

Hyperbolic Manifold Regression

Gian Marconi, Carlo Ciliberto, Lorenzo Rosasco

Computing Tight Differential Privacy Guarantees Using FFT

Antti Koskela, Joonas Jälkö, Antti Honkela

Precision-Recall Curves Using Information Divergence Frontiers

Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet

A Primal-Dual Solver for Large-Scale Tracking-by-Assignment

Stefan Haller, Mangal Prakash, Lisa Hutschenreiter, Tobias Pietzsch, Carsten Rother

Momentum in Reinforcement Learning

Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist

Private k-Means Clustering with Stability Assumptions

Moshe Shechner, Or Sheffet, Uri Stemmer

Lipschitz Continuous Autoencoders in Application to Anomaly Detection

Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik

Hypothesis Testing Interpretations and Renyi Differential Privacy

Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato

Bandit optimisation of functions in the Matérn kernel RKHS

David Janz, David Burt, Javier Gonzalez

Thresholding Graph Bandits with GrAPL

Daniel LeJeune, Gautam Dasarathy, Richard Baraniuk

Learning Rate Adaptation for Differentially Private Learning

Antti Koskela, Antti Honkela

A PTAS for the Bayesian Thresholding Bandit Problem

Jian Peng, Yue Qin, Yadi Wei, Yuan Zhou

Interpretable Companions for Black-Box Models

Danqing Pan, Tong Wang, Satoshi Hara