AISTATS 2020

423 papers

Nonmyopic Gaussian Process Optimization with Macro-Actions

Dmitrii Kharkovskii, Chun Kai Ling, Bryan Kian Hsiang Low

A Framework for Sample Efficient Interval Estimation with Control Variates

Shengjia Zhao, Christopher Yeh, Stefano Ermon

Revisiting Stochastic Extragradient

Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Yura Malitsky

Stein Variational Inference for Discrete Distributions

JUN HAN, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng

Auditing ML Models for Individual Bias and Unfairness

Songkai Xue, Mikhail Yurochkin, Yuekai Sun

On Minimax Optimality of GANs for Robust Mean Estimation

Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu

Approximate Cross-validation: Guarantees for Model Assessment and Selection

Ashia Wilson, Maximilian Kasy, Lester Mackey

Tighter Theory for Local SGD on Identical and Heterogeneous Data

Ahmed Khaled, Konstantin Mishchenko, Peter Richtarik

On the Sample Complexity of Learning Sum-Product Networks

Ishaq Aden-Ali, Hassan Ashtiani

Multi-attribute Bayesian optimization with interactive preference learning

Raul Astudillo, Peter Frazier

Finite-Time Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation

Jun Sun, Gang Wang, Georgios B. Giannakis, Qinmin Yang, Zaiyue Yang

Permutation Invariant Graph Generation via Score-Based Generative Modeling

Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover

A Theoretical and Practical Framework for Regression and Classification from Truncated Samples

Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis

Graph Coarsening with Preserved Spectral Properties

Yu Jin, Andreas Loukas, Joseph JaJa

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models

Christian Weilbach, Boyan Beronov, Frank Wood, William Harvey

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization

Dongruo Zhou, Yuan Cao, Quanquan Gu

Prediction Focused Topic Models via Feature Selection

Jason Ren, Russell Kunes, Finale Doshi-velez

Online Continuous DR-Submodular Maximization with Long-Term Budget Constraints

Omid Sadeghi, Maryam Fazel

Measuring Mutual Information Between All Pairs of Variables in Subquadratic Complexity

Mohsen Ferdosi, Arash Gholamidavoodi, Hosein Mohimani

Budget-Constrained Bandits over General Cost and Reward Distributions

Semih Cayci, Atilla Eryilmaz, R Srikant

Sublinear Optimal Policy Value Estimation in Contextual Bandits

Weihao Kong, Emma Brunskill, Gregory Valiant

Automatic Differentiation of Sketched Regression

Hang Liao, Barak A. Pearlmutter, Vamsi K. Potluru, David P. Woodruff

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare

Adaptive, Distribution-Free Prediction Intervals for Deep Networks

Danijel Kivaranovic, Kory D. Johnson, Hannes Leeb

Gaussianization Flows

Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon

Scalable Nonparametric Factorization for High-Order Interaction Events

Zhimeng Pan, Zheng Wang, Shandian Zhe

Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks

Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak

Stochastic Variance-Reduced Algorithms for PCA with Arbitrary Mini-Batch Sizes

Cheolmin Kim, Diego Klabjan

Regularized Autoencoders via Relaxed Injective Probability Flow

Abhishek Kumar, Ben Poole, Kevin Murphy

Best-item Learning in Random Utility Models with Subset Choices

Aadirupa Saha, Aditya Gopalan

Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems

Molei Tao, Tomoki Ohsawa

Prior-aware Composition Inference for Spectral Topic Models

Moontae Lee, David Bindel, David Mimno

LdSM: Logarithm-depth Streaming Multi-label Decision Trees

Maryam Majzoubi, Anna Choromanska

Invertible Generative Modeling using Linear Rational Splines

Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie

Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components

Christian Carmona, Geoff Nicholls

Multi-level Gaussian Graphical Models Conditional on Covariates

Gi Bum Kim, Seyoung Kim

Towards Competitive N-gram Smoothing

Moein Falahatgar, Mesrob Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati

Fair Correlation Clustering

Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian

Stochastic Neural Network with Kronecker Flow

Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste

Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation

Shuhang Chen, Adithya Devraj, Ana Busic, Sean Meyn

Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations

Yunfeng Cai, Ping Li

Expressiveness and Learning of Hidden Quantum Markov Models

Sandesh Adhikary, Siddarth Srinivasan, Geoff Gordon, Byron Boots

Optimal Deterministic Coresets for Ridge Regression

Praneeth Kacham, David Woodruff

AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning

Rizal Fathony, Zico Kolter

Optimal Algorithms for Multiplayer Multi-Armed Bandits

Po-An Wang, Alexandre Proutiere, Kaito Ariu, Yassir Jedra, Alessio Russo

Budget Learning via Bracketing

Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama

Ordering-Based Causal Structure Learning in the Presence of Latent Variables

Daniel Bernstein, Basil Saeed, Chandler Squires, Caroline Uhler

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Amir Zandieh, Navid Nouri, Ameya Velingker, Michael Kapralov, Ilya Razenshteyn

A Diversity-aware Model for Majority Vote Ensemble Accuracy

Bob Durrant, Nick Lim

Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions

Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki

Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data

Simao Eduardo, Alfredo Nazabal, Christopher K. I. Williams, Charles Sutton

Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes

Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang, Corey Chivers, Michael Draugelis

A Robust Univariate Mean Estimator is All You Need

Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar

Convex Geometry of Two-Layer ReLU Networks: Implicit Autoencoding and Interpretable Models

Tolga Ergen, Mert Pilanci

One Sample Stochastic Frank-Wolfe

Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge

Bryan Andrews, Peter Spirtes, Gregory F. Cooper

Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer

Yanshuai Cao, Peng Xu

Stochastic Recursive Variance-Reduced Cubic Regularization Methods

Dongruo Zhou, Quanquan Gu

Differentiable Causal Backdoor Discovery

Limor Gultchin, Matt Kusner, Varun Kanade, Ricardo Silva

Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification

Zhengjue Wang, Chaojie Wang, Hao Zhang, Zhibin Duan, Mingyuan Zhou

Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning

Ming Yin, Yu-Xiang Wang

Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks

Alexander Levine, Soheil Feizi

Gaussian Sketching yields a J-L Lemma in RKHS

Samory Kpotufe, Bharath Sriperumbudur

General Identification of Dynamic Treatment Regimes Under Interference

Eli Sherman, David Arbour, Ilya Shpitser

Learnable Bernoulli Dropout for Bayesian Deep Learning

Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh, Mingyuan Zhou, Xiaoning Qian

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

Zepeng Huo, Arash PakBin, Xiaohan Chen, Nathan Hurley, Ye Yuan

Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans

Scalable Gradients for Stochastic Differential Equations

Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud

Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems

Osbert Bastani

Online Batch Decision-Making with High-Dimensional Covariates

Chi-Hua Wang, Guang Cheng

Federated Heavy Hitters Discovery with Differential Privacy

Wennan Zhu, Peter Kairouz, Brendan Mcmahan, Haicheng Sun, Wei Li

Learning spectrograms with convolutional spectral kernels

Zheyang Shen, Markus Heinonen, Samuel Kaski

Adversarial Risk Bounds through Sparsity based Compression

Emilio Balda, Niklas Koep, Arash Behboodi, Rudolf Mathar

The Power of Batching in Multiple Hypothesis Testing

Tijana Zrnic, Daniel Jiang, Aaditya Ramdas, Michael Jordan

Adversarial Robustness of Flow-Based Generative Models

Phillip Pope, Yogesh Balaji, Soheil Feizi

A single algorithm for both restless and rested rotting bandits

Julien Seznec, Pierre Menard, Alessandro Lazaric, Michal Valko

Hamiltonian Monte Carlo Swindles

Dan Piponi, Matthew Hoffman, Pavel Sountsov

Orthogonal Gradient Descent for Continual Learning

Mehrdad Farajtabar, Navid Azizan, Alex Mott, Ang Li

A Theoretical Case Study of Structured Variational Inference for Community Detection

Mingzhang Yin, Y. X. Rachel Wang, Purnamrita Sarkar

Sharp Asymptotics and Optimal Performance for Inference in Binary Models

Hossein Taheri, Ramtin Pedarsani, CHRISTOS THRAMPOULIDIS

A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Ziyu Wang, Shuyu Cheng, Li Yueru, Jun Zhu, Bo Zhang

Prophets, Secretaries, and Maximizing the Probability of Choosing the Best

Hossein Esfandiari, MohammadTaghi Hajiaghayi, Brendan Lucier, Michael Mitzenmacher

A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option

P Sharoff, Nishant Mehta, Ravi Ganti

Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free

Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Distributed, partially collapsed MCMC for Bayesian Nonparametrics

Kumar Avinava Dubey, Michael Zhang, Eric Xing, Sinead Williamson

Constructing a provably adversarially-robust classifier from a high accuracy one

Grzegorz Gluch, Rüdiger Urbanke

An Asymptotic Rate for the LASSO Loss

Cynthia Rush

Logistic regression with peer-group effects via inference in higher-order Ising models

Constantinos Daskalakis, Nishanth Dikkala, Ioannis Panageas

Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling

Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi

Amortized Inference of Variational Bounds for Learning Noisy-OR

Yiming Yan, Melissa Ailem, Fei Sha

An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise

Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan

Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions

Grégoire Mialon, Julien Mairal, Alexandre d’Aspremont

The Expressive Power of a Class of Normalizing Flow Models

Zhifeng Kong, Kamalika Chaudhuri

Optimal Approximation of Doubly Stochastic Matrices

Nikitas Rontsis, Paul Goulart

POPCORN: Partially Observed Prediction Constrained Reinforcement Learning

Joseph Futoma, Michael Hughes, Finale Doshi-velez

Learning piecewise Lipschitz functions in changing environments

Dravyansh Sharma, Maria-Florina Balcan, Travis Dick

Learning Ising and Potts Models with Latent Variables

Surbhi Goel

A Three Sample Hypothesis Test for Evaluating Generative Models

Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta

Adaptive Exploration in Linear Contextual Bandit

Botao Hao, Tor Lattimore, Csaba Szepesvári

The Implicit Regularization of Ordinary Least Squares Ensembles

Daniel LeJeune, Hamid Javadi, Richard Baraniuk