AISTATS 2019

360 papers

Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit

Shengyu Zhu, Biao Chen, Pengfei Yang, Zhitang Chen

Gain estimation of linear dynamical systems using Thompson Sampling

Matias I. Müller, Cristian R. Rojas

Learning One-hidden-layer ReLU Networks via Gradient Descent

Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu

Training a Spiking Neural Network with Equilibrium Propagation

Peter O’Connor, Efstratios Gavves, Max Welling

Fast and Robust Shortest Paths on Manifolds Learned from Data

Georgios Arvanitidis, Soren Hauberg, Philipp Hennig, Michael Schober

Nonconvex Matrix Factorization from Rank-One Measurements

Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi

Iterative Bayesian Learning for Crowdsourced Regression

Jungseul Ok, Sewoong Oh, Yunhun Jang, Jinwoo Shin, Yung Yi

Learning Influence-Receptivity Network Structure with Guarantee

Ming Yu, Varun Gupta, Mladen Kolar

Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes

Tongfei Chen, Jiri Navratil, Vijay Iyengar, Karthikeyan Shanmugam

Projection Free Online Learning over Smooth Sets

Kfir Levy, Andreas Krause

Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

Filip Roos, Philipp Hennig

Adversarial Variational Optimization of Non-Differentiable Simulators

Gilles Louppe, Joeri Hermans, Kyle Cranmer

Black Box Quantiles for Kernel Learning

Anthony Tompkins, Ransalu Senanayake, Philippe Morere, Fabio Ramos

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors

Atsushi Nitanda, Taiji Suzuki

Wasserstein regularization for sparse multi-task regression

Hicham Janati, Marco Cuturi, Alexandre Gramfort

Learning Rules-First Classifiers

Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

Angelo Porrello, Davide Abati, SIMONE CALDERARA, Rita Cucchiara

Towards Clustering High-dimensional Gaussian Mixture Clouds in Linear Running Time

Dan Kushnir, Shirin Jalali, Iraj Saniee

A Continuous-Time View of Early Stopping for Least Squares Regression

Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani

Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features

Julius Kügelgen, Alexander Mey, Marco Loog

Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs

Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause

Support Localization and the Fisher Metric for off-the-grid Sparse Regularization

Clarice Poon, Nicolas Keriven, Gabriel Peyré

Distilling Policy Distillation

Wojciech M. Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant Jayakumar, Grzegorz Swirszcz

Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms

Alihan Hüyük, Cem Tekin

Markov Properties of Discrete Determinantal Point Processes

Kayvan Sadeghi, Alessandro Rinaldo

Detection of Planted Solutions for Flat Satisfiability Problems

Quentin Berthet, Jordan Ellenberg

Infinite Task Learning in RKHSs

Romain Brault, Alex Lambert, Zoltan Szabo, Maxime Sangnier, Florence d’Alché-Buc

Adaptive Ensemble Prediction for Deep Neural Networks based on Confidence Level

Hiroshi Inoue

Restarting Frank-Wolfe

Thomas Kerdreux, Alexandre d’Aspremont, Sebastian Pokutta

Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation

Jian Zhang, Avner May, Tri Dao, Christopher Re

Feature subset selection for the multinomial logit model via mixed-integer optimization

Shunsuke Kamiya, Ryuhei Miyashiro, Yuichi Takano

From Cost-Sensitive Classification to Tight F-measure Bounds

Kevin Bascol, Rémi Emonet, Elisa Fromont, Amaury Habrard, Guillaume Metzler

Model Consistency for Learning with Mirror-Stratifiable Regularizers

Jalal Fadili, Guillaume Garrigos, Jérôme Malick, Gabriel Peyré

Subsampled Renyi Differential Privacy and Analytical Moments Accountant

Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan

Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy

Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi

No-regret algorithms for online k-submodular maximization

Tasuku Soma

Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron

Sharan Vaswani, Francis Bach, Mark Schmidt

Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator

Seyoon Ko, Joong-Ho Won

Bayesian optimisation under uncertain inputs

Rafael Oliveira, Lionel Ott, Fabio Ramos

Towards Efficient Data Valuation Based on the Shapley Value

Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes

Auto-Encoding Total Correlation Explanation

Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan

Online Multiclass Boosting with Bandit Feedback

Daniel T. Zhang, Young Hun Jung, Ambuj Tewari

A Swiss Army Infinitesimal Jackknife

Ryan Giordano, William Stephenson, Runjing Liu, Michael Jordan, Tamara Broderick

XBART: Accelerated Bayesian Additive Regression Trees

Jingyu He, Saar Yalov, P. Richard Hahn

Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication

Jayadev Acharya, Ziteng Sun, Huanyu Zhang

Are we there yet? Manifold identification of gradient-related proximal methods

Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt

Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex

Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov

SPONGE: A generalized eigenproblem for clustering signed networks

Mihai Cucuringu, Peter Davies, Aldo Glielmo, Hemant Tyagi

Learning to Optimize under Non-Stationarity

Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu

Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent

Yifan Wu, Barnabás Póczos, Aarti Singh

Autoencoding any Data through Kernel Autoencoders

Pierre Laforgue, Stéphan Clémençon, Florence d’Alché-Buc

On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition

Marco Mondelli, Andrea Montanari

Conditional Sparse L_p-norm Regression With Optimal Probability

John Hainline, Brendan Juba, Hai S. Le, David Woodruff

Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach

Ryo Karakida, Shotaro Akaho, Shun-ichi Amari

Can You Trust This Prediction? Auditing Pointwise Reliability After Learning

Peter Schulam, Suchi Saria

$β^3$-IRT: A New Item Response Model and its Applications

Yu Chen, Telmo Silva Filho, Ricardo B. Prudencio, Tom Diethe, Peter Flach

Learning Invariant Representations with Kernel Warping

Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang

Bandit Online Learning with Unknown Delays

Bingcong Li, Tianyi Chen, Georgios B. Giannakis

On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes

Xiaoyu Li, Francesco Orabona

Active Exploration in Markov Decision Processes

Jean Tarbouriech, Alessandro Lazaric

Conditionally Independent Multiresolution Gaussian Processes

Jalil Taghia, Thomas Schön

Conservative Exploration using Interleaving

Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru

Correcting the bias in least squares regression with volume-rescaled sampling

Michal Derezinski, Manfred K. Warmuth, Daniel Hsu

Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models

Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S.v.n. Vishwanathan

Generalized Boltzmann Machine with Deep Neural Structure

Yingru Liu, Dongliang Xie, Xin Wang

On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition

Zhehui Chen, Xingguo Li, Lin Yang, Jarvis Haupt, Tuo Zhao

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks

Tengyuan Liang, James Stokes

Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives

Hadrien Hendrikx, Francis Bach, Laurent Massoulié

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks

Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, James Stokes

Attenuating Bias in Word vectors

Sunipa Dev, Jeff Phillips

Deep Neural Networks Learn Non-Smooth Functions Effectively

Masaaki Imaizumi, Kenji Fukumizu

Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Aapo Hyvärinen, Hiroaki Sasaki, Richard Turner

Optimal Transport for Multi-source Domain Adaptation under Target Shift

Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

George Papamakarios, David Sterratt, Iain Murray

On Kernel Derivative Approximation with Random Fourier Features

Zoltan Szabo, Bharath Sriperumbudur

A new evaluation framework for topic modeling algorithms based on synthetic corpora

Hanyu Shi, Martin Gerlach, Isabel Diersen, Doug Downey, Luis Amaral

Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization

Jonas Köhler, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Ming Zhou

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick

SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity

Luca Ambrogioni, Patrick Ebel, Max Hinne, Umut Güçlü, Marcel Gerven

Forward Amortized Inference for Likelihood-Free Variational Marginalization

Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva Borne, Yağmur Güçlütürk

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior

Gaël Letarte, Emilie Morvant, Pascal Germain

A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure

Juho Lee, Lancelot James, Seungjin Choi, Francois Caron

Gaussian Process Latent Variable Alignment Learning

Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell

Nonlinear Acceleration of Primal-Dual Algorithms

Raghu Bollapragada, Damien Scieur, Alexandre d’Aspremont

Bayesian Learning of Neural Network Architectures

Georgi Dikov, Justin Bayer

Linear Queries Estimation with Local Differential Privacy

Raef Bassily

Classification using margin pursuit

Matthew J. Holland

Robust descent using smoothed multiplicative noise

Matthew J. Holland

Fisher Information and Natural Gradient Learning in Random Deep Networks

Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi

Defending against Whitebox Adversarial Attacks via Randomized Discretization

Yuchen Zhang, Percy Liang

Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks

Yue Yu, Jiaxiang Wu, Junzhou Huang

Robust Graph Embedding with Noisy Link Weights

Akifumi Okuno, Hidetoshi Shimodaira

High-dimensional Mixed Graphical Model with Ordinal Data: Parameter Estimation and Statistical Inference

Huijie Feng, Yang Ning

Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability

Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira

Reversible Jump Probabilistic Programming

David A. Roberts, Marcus Gallagher, Thomas Taimre

Optimal Testing in the Experiment-rich Regime

Sven Schmit, Virag Shah, Ramesh Johari

On Target Shift in Adversarial Domain Adaptation

Yitong Li, michael Murias, samantha Major, geraldine Dawson, David Carlson

Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms

Mathieu Blondel, Andre Martins, Vlad Niculae

Temporal Quilting for Survival Analysis

Changhee Lee, William Zame, Ahmed Alaa, Mihaela Schaar

Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators

Oren Mangoubi, Aaron Smith