AISTATS 2019

360 papers

Computation Efficient Coded Linear Transform

Sinong Wang, Jiashang Liu, Ness Shroff, Pengyu Yang

What made you do this? Understanding black-box decisions with sufficient input subsets

Brandon Carter, Jonas Mueller, Siddhartha Jain, David Gifford

On Connecting Stochastic Gradient MCMC and Differential Privacy

Bai Li, Changyou Chen, Hao Liu, Lawrence Carin

Optimization of Inf-Convolution Regularized Nonconvex Composite Problems

Emanuel Laude, Tao Wu, Daniel Cremers

Efficient Inference in Multi-task Cox Process Models

Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla

Support and Invertibility in Domain-Invariant Representations

Fredrik D. Johansson, David Sontag, Rajesh Ranganath

A Robust Zero-Sum Game Framework for Pool-based Active Learning

Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang

Distributionally Robust Submodular Maximization

Matthew Staib, Bryan Wilder, Stefanie Jegelka

Testing Conditional Independence on Discrete Data using Stochastic Complexity

Alexander Marx, Jilles Vreeken

Local Saddle Point Optimization: A Curvature Exploitation Approach

Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann

Efficient Bayesian Experimental Design for Implicit Models

Steven Kleinegesse, Michael U. Gutmann

An Optimal Algorithm for Stochastic and Adversarial Bandits

Julian Zimmert, Yevgeny Seldin

Sparse Multivariate Bernoulli Processes in High Dimensions

Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash Amini, Sundeep Rangan, Alyson K. Fletcher

Lifelong Optimization with Low Regret

Yi-Shan Wu, Po-An Wang, Chi-Jen Lu

A Thompson Sampling Algorithm for Cascading Bandits

Wang Chi Cheung, Vincent Tan, Zixin Zhong

An Optimal Algorithm for Stochastic Three-Composite Optimization

Renbo Zhao, William B. Haskell, Vincent Y. F. Tan

Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit

Yang Cao, Zheng Wen, Branislav Kveton, Yao Xie

Foundations of Sequence-to-Sequence Modeling for Time Series

Zelda Mariet, Vitaly Kuznetsov

Robust Matrix Completion from Quantized Observations

Jie Shen, Pranjal Awasthi, Ping Li

Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm

Nikos Kargas, Nicholas D. Sidiropoulos

Analysis of Network Lasso for Semi-Supervised Regression

Alexander Jung, Natalia Vesselinova

Performance Metric Elicitation from Pairwise Classifier Comparisons

Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo

Blind Demixing via Wirtinger Flow with Random Initialization

Jialin Dong, Yuanming Shi

Vine copula structure learning via Monte Carlo tree search

Bo Chang, Shenyi Pan, Harry Joe

Distributed Inexact Newton-type Pursuit for Non-convex Sparse Learning

Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Junzhou Huang

Pathwise Derivatives for Multivariate Distributions

Martin Jankowiak, Theofanis Karaletsos

Modularity-based Sparse Soft Graph Clustering

Alexandre Hollocou, Thomas Bonald, marc lelarge

Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems

Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar

Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches

Filip Hanzely, Peter Richtarik

Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity

Dan Garber

Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems

Dan Garber, Atara Kaplan

Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs

Rishabh Iyer, Jeffrey Bilmes

Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang

Sketching for Latent Dirichlet-Categorical Models

Joseph Tassarotti, Jean-Baptiste Tristan, Michael Wick

Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain

Quentin Berthet, Varun Kanade

KAMA-NNs: Low-dimensional Rotation Based Neural Networks

Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang

A Stein–Papangelou Goodness-of-Fit Test for Point Processes

Jiasen Yang, Vinayak Rao, Jennifer Neville

Block Stability for MAP Inference

Hunter Lang, David Sontag, Aravindan Vijayaraghavan

Greedy and IHT Algorithms for Non-convex Optimization with Monotone Costs of Non-zeros

Shinsaku Sakaue

Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity

Simon S. Du, Wei Hu

Orthogonal Estimation of Wasserstein Distances

Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos

Efficient Linear Bandits through Matrix Sketching

Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-bianchi

Unbiased Implicit Variational Inference

Michalis K. Titsias, Francisco Ruiz

Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Models

Gunwoong Park, Hyewon Park

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang

Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds

Pan Zhou, Xiao-Tong Yuan, Jiashi Feng

Towards a Theoretical Understanding of Hashing-Based Neural Nets

Yibo Lin, Zhao Song, Lin F. Yang

On the Interaction Effects Between Prediction and Clustering

Matt Barnes, Artur Dubrawski

Interaction Detection with Bayesian Decision Tree Ensembles

Junliang Du, Antonio R. Linero

Inferring Multidimensional Rates of Aging from Cross-Sectional Data

Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec

Scalable Thompson Sampling via Optimal Transport

Ruiyi Zhang, Zheng Wen, Changyou Chen, Chen Fang, Tong Yu

Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers

Marcel Hirt, Petros Dellaportas

Resampled Priors for Variational Autoencoders

Matthias Bauer, Andriy Mnih

Boosting Transfer Learning with Survival Data from Heterogeneous Domains

Alexis Bellot, Mihaela van der Schaar

Error bounds for sparse classifiers in high-dimensions

Antoine Dedieu

Risk-Averse Stochastic Convex Bandit

Adrian Rivera Cardoso, Huan Xu

Gaussian Regression with Convex Constraints

Matey Neykov

Tossing Coins Under Monotonicity

Matey Neykov

Optimal Noise-Adding Mechanism in Additive Differential Privacy

Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar

Proximal Splitting Meets Variance Reduction

Fabian Pedregosa, Kilian Fatras, Mattia Casotto