Volume 89: The 22nd International Conference on Artificial Intelligence and Statistics, 16-18 April 2019,


Editors: Kamalika Chaudhuri, Masashi Sugiyama


Proximal Splitting Meets Variance Reduction

Fabian Pedregosa, Kilian Fatras, Mattia Casotto ; PMLR 89:1-10

Optimal Noise-Adding Mechanism in Additive Differential Privacy

Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar ; PMLR 89:11-20

Tossing Coins Under Monotonicity

Matey Neykov ; PMLR 89:21-30

Gaussian Regression with Convex Constraints

Matey Neykov ; PMLR 89:31-38

Risk-Averse Stochastic Convex Bandit

Adrian Rivera Cardoso, Huan Xu ; PMLR 89:39-47

Error bounds for sparse classifiers in high-dimensions

Antoine Dedieu ; PMLR 89:48-56

Boosting Transfer Learning with Survival Data from Heterogeneous Domains

Alexis Bellot, Mihaela Schaar ; PMLR 89:57-65

Resampled Priors for Variational Autoencoders

Matthias Bauer, Andriy Mnih ; PMLR 89:66-75

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

Marcel Hirt, Petros Dellaportas ; PMLR 89:76-86

Scalable Thompson Sampling via Optimal Transport

Ruiyi Zhang, Zheng Wen, Changyou Chen, Chen Fang, Tong Yu, Lawrence Carin ; PMLR 89:87-96

Inferring Multidimensional Rates of Aging from Cross-Sectional Data

Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson, Percy Liang ; PMLR 89:97-107

Interaction Detection with Bayesian Decision Tree Ensembles

Junliang Du, Antonio R. Linero ; PMLR 89:108-117

On the Interaction Effects Between Prediction and Clustering

Matt Barnes, Artur Dubrawski ; PMLR 89:118-126

Towards a Theoretical Understanding of Hashing-Based Neural Nets

Yibo Lin, Zhao Song, Lin F. Yang ; PMLR 89:127-137

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

Pan Zhou, Xiao-Tong Yuan, Jiashi Feng ; PMLR 89:138-147

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, Frank Wood ; PMLR 89:148-157

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

Gunwoong Park, Hyewon Park ; PMLR 89:158-166

Unbiased Implicit Variational Inference

Michalis K. Titsias, Francisco Ruiz ; PMLR 89:167-176

Efficient Linear Bandits through Matrix Sketching

Ilja Kuzborskij, Leonardo Cella, Nicolò Cesa-Bianchi ; PMLR 89:177-185

Orthogonal Estimation of Wasserstein Distances

Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, Adrian Weller ; PMLR 89:186-195

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

Simon S. Du, Wei Hu ; PMLR 89:196-205

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

Shinsaku Sakaue ; PMLR 89:206-215

Block Stability for MAP Inference

Hunter Lang, David Sontag, Aravindan Vijayaraghavan ; PMLR 89:216-225

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

Jiasen Yang, Vinayak Rao, Jennifer Neville ; PMLR 89:226-235

KAMA-NNs: Low-dimensional Rotation Based Neural Networks

Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang ; PMLR 89:236-245

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

Quentin Berthet, Varun Kanade ; PMLR 89:246-255

Sketching for Latent Dirichlet-Categorical Models

Joseph Tassarotti, Jean-Baptiste Tristan, Michael Wick ; PMLR 89:256-265

Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian ; PMLR 89:266-275

Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs

Rishabh Iyer, Jeffrey Bilmes ; PMLR 89:276-285

Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems

Dan Garber, Atara Kaplan ; PMLR 89:286-294

Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity

Dan Garber ; PMLR 89:295-303

Accelerated Coordinate Descent with Arbitrary Sampling and Best Rates for Minibatches

Filip Hanzely, Peter Richtarik ; PMLR 89:304-312

Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems

Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar ; PMLR 89:313-322

Modularity-based Sparse Soft Graph Clustering

Alexandre Hollocou, Thomas Bonald, Marc Lelarge ; PMLR 89:323-332

Pathwise Derivatives for Multivariate Distributions

Martin Jankowiak, Theofanis Karaletsos ; PMLR 89:333-342

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

Bo Liu, Xiao-Tong Yuan, Lezi Wang, Qingshan Liu, Junzhou Huang, Dimitris N. Metaxas ; PMLR 89:343-352

Vine copula structure learning via Monte Carlo tree search

Bo Chang, Shenyi Pan, Harry Joe ; PMLR 89:353-361

Blind Demixing via Wirtinger Flow with Random Initialization

Jialin Dong, Yuanming Shi ; PMLR 89:362-370

Performance Metric Elicitation from Pairwise Classifier Comparisons

Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi Koyejo ; PMLR 89:371-379

Analysis of Network Lasso for Semi-Supervised Regression

Alexander Jung, Natalia Vesselinova ; PMLR 89:380-387

Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm

Nikos Kargas, Nicholas D. Sidiropoulos ; PMLR 89:388-396

Robust Matrix Completion from Quantized Observations

Jie Shen, Pranjal Awasthi, Ping Li ; PMLR 89:397-407

Foundations of Sequence-to-Sequence Modeling for Time Series

Zelda Mariet, Vitaly Kuznetsov ; PMLR 89:408-417

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

Yang Cao, Zheng Wen, Branislav Kveton, Yao Xie ; PMLR 89:418-427

An Optimal Algorithm for Stochastic Three-Composite Optimization

Renbo Zhao, William B. Haskell, Vincent Y. F. Tan ; PMLR 89:428-437

A Thompson Sampling Algorithm for Cascading Bandits

Wang Chi Cheung, Vincent Tan, Zixin Zhong ; PMLR 89:438-447

Lifelong Optimization with Low Regret

Yi-Shan Wu, Po-An Wang, Chi-Jen Lu ; PMLR 89:448-456

Sparse Multivariate Bernoulli Processes in High Dimensions

Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash Amini, Sundeep Rangan, Alyson K. Fletcher ; PMLR 89:457-466

An Optimal Algorithm for Stochastic and Adversarial Bandits

Julian Zimmert, Yevgeny Seldin ; PMLR 89:467-475

Efficient Bayesian Experimental Design for Implicit Models

Steven Kleinegesse, Michael U. Gutmann ; PMLR 89:476-485

Local Saddle Point Optimization: A Curvature Exploitation Approach

Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann ; PMLR 89:486-495

Testing Conditional Independence on Discrete Data using Stochastic Complexity

Alexander Marx, Jilles Vreeken ; PMLR 89:496-505

Distributionally Robust Submodular Maximization

Matthew Staib, Bryan Wilder, Stefanie Jegelka ; PMLR 89:506-516

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

Dixian Zhu, Zhe Li, Xiaoyu Wang, Boqing Gong, Tianbao Yang ; PMLR 89:517-526

Support and Invertibility in Domain-Invariant Representations

Fredrik D. Johansson, David Sontag, Rajesh Ranganath ; PMLR 89:527-536

Efficient Inference in Multi-task Cox Process Models

Virginia Aglietti, Theodoros Damoulas, Edwin V. Bonilla ; PMLR 89:537-546

Optimization of Inf-Convolution Regularized Nonconvex Composite Problems

Emanuel Laude, Tao Wu, Daniel Cremers ; PMLR 89:547-556

On Connecting Stochastic Gradient MCMC and Differential Privacy

Bai Li, Changyou Chen, Hao Liu, Lawrence Carin ; PMLR 89:557-566

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

Brandon Carter, Jonas Mueller, Siddhartha Jain, David Gifford ; PMLR 89:567-576

Computation Efficient Coded Linear Transform

Sinong Wang, Jiashang Liu, Ness Shroff, Pengyu Yang ; PMLR 89:577-585

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

Oren Mangoubi, Aaron Smith ; PMLR 89:586-595

Temporal Quilting for Survival Analysis

Changhee Lee, William Zame, Ahmed Alaa, Mihaela Schaar ; PMLR 89:596-605

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

Mathieu Blondel, Andre Martins, Vlad Niculae ; PMLR 89:606-615

On Target Shift in Adversarial Domain Adaptation

Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David Carlson ; PMLR 89:616-625

Optimal Testing in the Experiment-rich Regime

Sven Schmit, Virag Shah, Ramesh Johari ; PMLR 89:626-633

Reversible Jump Probabilistic Programming

David A. Roberts, Marcus Gallagher, Thomas Taimre ; PMLR 89:634-643

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

Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira ; PMLR 89:644-653

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

Huijie Feng, Yang Ning ; PMLR 89:654-663

Robust Graph Embedding with Noisy Link Weights

Akifumi Okuno, Hidetoshi Shimodaira ; PMLR 89:664-673

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

Yue Yu, Jiaxiang Wu, Junzhou Huang ; PMLR 89:674-683

Defending against Whitebox Adversarial Attacks via Randomized Discretization

Yuchen Zhang, Percy Liang ; PMLR 89:684-693

Fisher Information and Natural Gradient Learning in Random Deep Networks

Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi ; PMLR 89:694-702

Robust descent using smoothed multiplicative noise

Matthew J. Holland ; PMLR 89:703-711

Classification using margin pursuit

Matthew J. Holland ; PMLR 89:712-720

Linear Queries Estimation with Local Differential Privacy

Raef Bassily ; PMLR 89:721-729

Bayesian Learning of Neural Network Architectures

Georgi Dikov, Justin Bayer ; PMLR 89:730-738

Nonlinear Acceleration of Primal-Dual Algorithms

Raghu Bollapragada, Damien Scieur, Alexandre d’Aspremont ; PMLR 89:739-747

Gaussian Process Latent Variable Alignment Learning

Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell ; PMLR 89:748-757

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

Juho Lee, Lancelot James, Seungjin Choi, Francois Caron ; PMLR 89:758-767

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior

Gaël Letarte, Emilie Morvant, Pascal Germain ; PMLR 89:768-776

Forward Amortized Inference for Likelihood-Free Variational Marginalization

Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva Borne, Yaǧmur Güçlütürk, Max Hinne, Eric Maris, Marcel Gerven ; PMLR 89:777-786

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

Luca Ambrogioni, Patrick Ebel, Max Hinne, Umut Güçlü, Marcel Gerven, Eric Maris ; PMLR 89:787-795

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

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick ; PMLR 89:796-805

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

Jonas Kohler, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr ; PMLR 89:806-815

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

Hanyu Shi, Martin Gerlach, Isabel Diersen, Doug Downey, Luis Amaral ; PMLR 89:816-826

On Kernel Derivative Approximation with Random Fourier Features

Zoltan Szabo, Bharath Sriperumbudur ; PMLR 89:827-836

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

George Papamakarios, David Sterratt, Iain Murray ; PMLR 89:837-848

Optimal Transport for Multi-source Domain Adaptation under Target Shift

Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia ; PMLR 89:849-858

Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Aapo Hyvarinen, Hiroaki Sasaki, Richard Turner ; PMLR 89:859-868

Deep Neural Networks Learn Non-Smooth Functions Effectively

Masaaki Imaizumi, Kenji Fukumizu ; PMLR 89:869-878

Attenuating Bias in Word vectors

Sunipa Dev, Jeff Phillips ; PMLR 89:879-887

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

Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, James Stokes ; PMLR 89:888-896

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

Hadrien Hendrikx, Francis Bach, Laurent Massoulie ; PMLR 89:897-906

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

Tengyuan Liang, James Stokes ; PMLR 89:907-915

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

Zhehui Chen, Xingguo Li, Lin Yang, Jarvis Haupt, Tuo Zhao ; PMLR 89:916-925

Generalized Boltzmann Machine with Deep Neural Structure

Yingru Liu, Dongliang Xie, Xin Wang ; PMLR 89:926-934

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

Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S.V.N. Vishwanathan, Inderjit Dhillon ; PMLR 89:935-943

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

Michal Derezinski, Manfred K. Warmuth, Daniel Hsu ; PMLR 89:944-953

Conservative Exploration using Interleaving

Sumeet Katariya, Branislav Kveton, Zheng Wen, Vamsi K. Potluru ; PMLR 89:954-963

Conditionally Independent Multiresolution Gaussian Processes

Jalil Taghia, Thomas Schön ; PMLR 89:964-973

Active Exploration in Markov Decision Processes

Jean Tarbouriech, Alessandro Lazaric ; PMLR 89:974-982

On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes

Xiaoyu Li, Francesco Orabona ; PMLR 89:983-992

Bandit Online Learning with Unknown Delays

Bingcong Li, Tianyi Chen, Georgios B. Giannakis ; PMLR 89:993-1002

Learning Invariant Representations with Kernel Warping

Yingyi Ma, Vignesh Ganapathiraman, Xinhua Zhang ; PMLR 89:1003-1012

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

Yu Chen, Telmo Silva Filho, Ricardo B. Prudencio, Tom Diethe, Peter Flach ; PMLR 89:1013-1021

Can You Trust This Prediction? Auditing Pointwise Reliability After Learning

Peter Schulam, Suchi Saria ; PMLR 89:1022-1031

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

Ryo Karakida, Shotaro Akaho, Shun-ichi Amari ; PMLR 89:1032-1041

Conditional Sparse $L_p$-norm Regression With Optimal Probability

John Hainline, Brendan Juba, Hai S. Le, David Woodruff ; PMLR 89:1042-1050

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

Marco Mondelli, Andrea Montanari ; PMLR 89:1051-1060

Autoencoding any Data through Kernel Autoencoders

Pierre Laforgue, Stéphan Clémençon, Florence d’Alche-Buc ; PMLR 89:1061-1069

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

Yifan Wu, Barnabas Poczos, Aarti Singh ; PMLR 89:1070-1078

Learning to Optimize under Non-Stationarity

Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu ; PMLR 89:1079-1087

SPONGE: A generalized eigenproblem for clustering signed networks

Mihai Cucuringu, Peter Davies, Aldo Glielmo, Hemant Tyagi ; PMLR 89:1088-1098

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

Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov ; PMLR 89:1099-1109

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

Yifan Sun, Halyun Jeong, Julie Nutini, Mark Schmidt ; PMLR 89:1110-1119

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

Jayadev Acharya, Ziteng Sun, Huanyu Zhang ; PMLR 89:1120-1129

XBART: Accelerated Bayesian Additive Regression Trees

Jingyu He, Saar Yalov, P. Richard Hahn ; PMLR 89:1130-1138

A Swiss Army Infinitesimal Jackknife

Ryan Giordano, William Stephenson, Runjing Liu, Michael Jordan, Tamara Broderick ; PMLR 89:1139-1147

Online Multiclass Boosting with Bandit Feedback

Daniel T. Zhang, Young Hun Jung, Ambuj Tewari ; PMLR 89:1148-1156

Auto-Encoding Total Correlation Explanation

Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan ; PMLR 89:1157-1166

Towards Efficient Data Valuation Based on the Shapley Value

Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, Costas J. Spanos ; PMLR 89:1167-1176

Bayesian optimisation under uncertain inputs

Rafael Oliveira, Lionel Ott, Fabio Ramos ; PMLR 89:1177-1184

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

Seyoon Ko, Joong-Ho Won ; PMLR 89:1185-1194

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

Sharan Vaswani, Francis Bach, Mark Schmidt ; PMLR 89:1195-1204

No-regret algorithms for online $k$-submodular maximization

Tasuku Soma ; PMLR 89:1205-1214

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

Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Salman A. Avestimehr ; PMLR 89:1215-1225

Subsampled Renyi Differential Privacy and Analytical Moments Accountant

Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan ; PMLR 89:1226-1235

Model Consistency for Learning with Mirror-Stratifiable Regularizers

Jalal Fadili, Guillaume Garrigos, Jérôme Malick, Gabriel Peyré ; PMLR 89:1236-1244

From Cost-Sensitive to Tight F-measure Bounds

Kevin Bascol, Rémi Emonet, Elisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban ; PMLR 89:1245-1253

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

Shunsuke Kamiya, Ryuhei Miyashiro, Yuichi Takano ; PMLR 89:1254-1263

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

Jian Zhang, Avner May, Tri Dao, Christopher Re ; PMLR 89:1264-1274

Restarting Frank-Wolfe

Thomas Kerdreux, Alexandre d’Aspremont, Sebastian Pokutta ; PMLR 89:1275-1283

Adaptive Ensemble Prediction for Deep Neural Networks based on Confidence Level

Hiroshi Inoue ; PMLR 89:1284-1293

Infinite Task Learning in RKHSs

Romain Brault, Alex Lambert, Zoltan Szabo, Maxime Sangnier, Florence d’Alche-Buc ; PMLR 89:1294-1302

Detection of Planted Solutions for Flat Satisfiability Problems

Quentin Berthet, Jordan Ellenberg ; PMLR 89:1303-1312

Markov Properties of Discrete Determinantal Point Processes

Kayvan Sadeghi, Alessandro Rinaldo ; PMLR 89:1313-1321

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

Alihan Huyuk, Cem Tekin ; PMLR 89:1322-1330

Distilling Policy Distillation

Wojciech M. Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant Jayakumar, Grzegorz Swirszcz, Max Jaderberg ; PMLR 89:1331-1340

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

Clarice Poon, Nicolas Keriven, Gabriel Peyré ; PMLR 89:1341-1350

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, Joachim M. Buhmann ; PMLR 89:1351-1360

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

Julius Kügelgen, Alexander Mey, Marco Loog ; PMLR 89:1361-1369

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

Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani ; PMLR 89:1370-1378

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

Dan Kushnir, Shirin Jalali, Iraj Saniee ; PMLR 89:1379-1387

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara ; PMLR 89:1388-1397

Learning Rules-First Classifiers

Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan ; PMLR 89:1398-1406

Wasserstein regularization for sparse multi-task regression

Hicham Janati, Marco Cuturi, Alexandre Gramfort ; PMLR 89:1407-1416

Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors

Atsushi Nitanda, Taiji Suzuki ; PMLR 89:1417-1426

Black Box Quantiles for Kernel Learning

Anthony Tompkins, Ransalu Senanayake, Philippe Morere, Fabio Ramos ; PMLR 89:1427-1437

Adversarial Variational Optimization of Non-Differentiable Simulators

Gilles Louppe, Joeri Hermans, Kyle Cranmer ; PMLR 89:1438-1447

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

Filip Roos, Philipp Hennig ; PMLR 89:1448-1457

Projection Free Online Learning over Smooth Sets

Kfir Levy, Andreas Krause ; PMLR 89:1458-1466

Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes

Tongfei Chen, Jiri Navratil, Vijay Iyengar, Karthikeyan Shanmugam ; PMLR 89:1467-1475

Learning Influence-Receptivity Network Structure with Guarantee

Ming Yu, Varun Gupta, Mladen Kolar ; PMLR 89:1476-1485

Iterative Bayesian Learning for Crowdsourced Regression

Jungseul Ok, Sewoong Oh, Yunhun Jang, Jinwoo Shin, Yung Yi ; PMLR 89:1486-1495

Nonconvex Matrix Factorization from Rank-One Measurements

Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi ; PMLR 89:1496-1505

Fast and Robust Shortest Paths on Manifolds Learned from Data

Georgios Arvanitidis, Soren Hauberg, Philipp Hennig, Michael Schober ; PMLR 89:1506-1515

Training a Spiking Neural Network with Equilibrium Propagation

Peter O’Connor, Efstratios Gavves, Max Welling ; PMLR 89:1516-1523

Learning One-hidden-layer ReLU Networks via Gradient Descent

Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu ; PMLR 89:1524-1534

Gain estimation of linear dynamical systems using Thompson Sampling

Matias I. Müller, Cristian R. Rojas ; PMLR 89:1535-1543

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

Shengyu Zhu, Biao Chen, Pengfei Yang, Zhitang Chen ; PMLR 89:1544-1553

Calibrating Deep Convolutional Gaussian Processes

Gia-Lac Tran, Edwin V. Bonilla, John Cunningham, Pietro Michiardi, Maurizio Filippone ; PMLR 89:1554-1563

Stochastic algorithms with descent guarantees for ICA

Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach ; PMLR 89:1564-1573

Sample Complexity of Sinkhorn Divergences

Aude Genevay, Lénaïc Chizat, Francis Bach, Marco Cuturi, Gabriel Peyré ; PMLR 89:1574-1583

Adaptive Gaussian Copula ABC

Yanzhi Chen, Michael U. Gutmann ; PMLR 89:1584-1592

Top Feasible Arm Identification

Julian Katz-Samuels, Clayton Scott ; PMLR 89:1593-1601

Direct Acceleration of SAGA using Sampled Negative Momentum

Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhi-Quan Luo ; PMLR 89:1602-1610

Does data interpolation contradict statistical optimality?

Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov ; PMLR 89:1611-1619

Inverting Supervised Representations with Autoregressive Neural Density Models

Charlie Nash, Nate Kushman, Christopher K.I. Williams ; PMLR 89:1620-1629

Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning

Guillaume Rabusseau, Tianyu Li, Doina Precup ; PMLR 89:1630-1639

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 ; PMLR 89:1640-1649

Differentially Private Online Submodular Minimization

Adrian Rivera Cardoso, Rachel Cummings ; PMLR 89:1650-1658

Semi-supervised clustering for de-duplication

Shrinu Kushagra, Shai Ben-David, Ihab Ilyas ; PMLR 89:1659-1667

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

Pierre Perrault, Vianney Perchet, Michal Valko ; PMLR 89:1668-1677

Statistical Learning under Nonstationary Mixing Processes

Steve Hanneke, Liu Yang ; PMLR 89:1678-1686

On Structure Priors for Learning Bayesian Networks

Ralf Eggeling, Jussi Viinikka, Aleksis Vuoksenmaa, Mikko Koivisto ; PMLR 89:1687-1695

Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs

Alexander Bauer, Shinichi Nakajima, Nico Goernitz, Klaus-Robert Müller ; PMLR 89:1696-1703

Sparse Feature Selection in Kernel Discriminant Analysis via Optimal Scoring

Alexander F. Lapanowski, Irina Gaynanova ; PMLR 89:1704-1713

Learning Natural Programs from a Few Examples in Real-Time

Nagarajan Natarajan, Danny Simmons, Naren Datha, Prateek Jain, Sumit Gulwani ; PMLR 89:1714-1722

Truncated Back-propagation for Bilevel Optimization

Amirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots ; PMLR 89:1723-1732

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data

Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz ; PMLR 89:1733-1742

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

Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari ; PMLR 89:1743-1752

Lifted Weight Learning of Markov Logic Networks Revisited

Ondrej Kuzelka, Vyacheslav Kungurtsev ; PMLR 89:1753-1761

Causal Discovery in the Presence of Missing Data

Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang ; PMLR 89:1762-1770

Learning Tree Structures from Noisy Data

Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate ; PMLR 89:1771-1782

Active multiple matrix completion with adaptive confidence sets

Andrea Locatelli, Alexandra Carpentier, Michal Valko ; PMLR 89:1783-1791

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar ; PMLR 89:1792-1801

Negative Momentum for Improved Game Dynamics

Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas ; PMLR 89:1802-1811

Deep learning with differential Gaussian process flows

Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski ; PMLR 89:1812-1821

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

Raj Agrawal, Trevor Campbell, Jonathan Huggins, Tamara Broderick ; PMLR 89:1822-1831

Large-Margin Classification in Hyperbolic Space

Hyunghoon Cho, Benjamin DeMeo, Jian Peng, Bonnie Berger ; PMLR 89:1832-1840

Generalizing the theory of cooperative inference

Pei Wang, Pushpi Paranamana, Patrick Shafto ; PMLR 89:1841-1850

MaxHedge: Maximizing a Maximum Online

Stephen Pasteris, Fabio Vitale, Kevin Chan, Shiqiang Wang, Mark Herbster ; PMLR 89:1851-1859

The Gaussian Process Autoregressive Regression Model (GPAR)

James Requeima, William Tebbutt, Wessel Bruinsma, Richard E. Turner ; PMLR 89:1860-1869

Towards Optimal Transport with Global Invariances

David Alvarez-Melis, Stefanie Jegelka, Tommi S. Jaakkola ; PMLR 89:1870-1879

Unsupervised Alignment of Embeddings with Wasserstein Procrustes

Edouard Grave, Armand Joulin, Quentin Berthet ; PMLR 89:1880-1890

Sequential Patient Recruitment and Allocation for Adaptive Clinical Trials

Onur Atan, William R. Zame, Mihaela Schaar ; PMLR 89:1891-1900

Probabilistic Forecasting with Spline Quantile Function RNNs

Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski ; PMLR 89:1901-1910

Exponential Weights on the Hypercube in Polynomial Time

Sudeep Raja Putta, Abhishek Shetty ; PMLR 89:1911-1919

Sharp Analysis of Learning with Discrete Losses

Alex Nowak, Francis Bach, Alessandro Rudi ; PMLR 89:1920-1929

Designing Optimal Binary Rating Systems

Nikhil Garg, Ramesh Johari ; PMLR 89:1930-1939

Stochastic Negative Mining for Learning with Large Output Spaces

Sashank J. Reddi, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Jiecao Chen, Sanjiv Kumar ; PMLR 89:1940-1949

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

Weihao Gao, Ashok V. Makkuva, Sewoong Oh, Pramod Viswanath ; PMLR 89:1950-1959

A Geometric Perspective on the Transferability of Adversarial Directions

Zachary Charles, Harrison Rosenberg, Dimitris Papailiopoulos ; PMLR 89:1960-1968

Non-linear process convolutions for multi-output Gaussian processes

Mauricio A. Alvarez, Wil Ward, Cristian Guarnizo ; PMLR 89:1969-1977

Lovasz Convolutional Networks

Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar ; PMLR 89:1978-1987

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

Rémy Degenne, Thomas Nedelec, Clement Calauzenes, Vianney Perchet ; PMLR 89:1988-1996

Gaussian Process Modulated Cox Processes under Linear Inequality Constraints

Andrés F. Lopez-lopera, ST John, Nicolas Durrande ; PMLR 89:1997-2006

Implicit Kernel Learning

Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabas Poczos ; PMLR 89:2007-2016

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

Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause ; PMLR 89:2017-2027

Variational Information Planning for Sequential Decision Making

Jason Pacheco, John Fisher ; PMLR 89:2028-2036

Renyi Differentially Private ERM for Smooth Objectives

Chen Chen, Jaewoo Lee, Dan Kifer ; PMLR 89:2037-2046

Projection-Free Bandit Convex Optimization

Lin Chen, Mingrui Zhang, Amin Karbasi ; PMLR 89:2047-2056

Provable Robustness of ReLU networks via Maximization of Linear Regions

Francesco Croce, Maksym Andriushchenko, Matthias Hein ; PMLR 89:2057-2066

Test without Trust: Optimal Locally Private Distribution Testing

Jayadev Acharya, Clement Canonne, Cody Freitag, Himanshu Tyagi ; PMLR 89:2067-2076

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

Mehrdad Ghadiri, Mark Schmidt ; PMLR 89:2077-2086

On Euclidean k-Means Clustering with alpha-Center Proximity

Amit Deshpande, Anand Louis, Apoorv Singh ; PMLR 89:2087-2095

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

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai ; PMLR 89:2096-2105

Safe Convex Learning under Uncertain Constraints

Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour ; PMLR 89:2106-2114

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

Noureddine El Karoui, Elizabeth Purdom ; PMLR 89:2115-2124

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

Jaime Roquero Gimenez, Amirata Ghorbani, James Zou ; PMLR 89:2125-2133

Training Variational Autoencoders with Buffered Stochastic Variational Inference

Rui Shu, Hung Bui, Jay Whang, Stefano Ermon ; PMLR 89:2134-2143

Regularized Contextual Bandits

Xavier Fontaine, Quentin Berthet, Vianney Perchet ; PMLR 89:2144-2153

Risk-Sensitive Generative Adversarial Imitation Learning

Jonathan Lacotte, Mohammad Ghavamzadeh, Yinlam Chow, Marco Pavone ; PMLR 89:2154-2163

Learning Controllable Fair Representations

Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon ; PMLR 89:2164-2173

Multi-Task Time Series Analysis applied to Drug Response Modelling

Alex Bird, Chris Williams, Christopher Hawthorne ; PMLR 89:2174-2183

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

Jaime Roquero Gimenez, James Zou ; PMLR 89:2184-2192

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

Arno Solin, Manon Kok ; PMLR 89:2193-2202

Distributional reinforcement learning with linear function approximation

Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra ; PMLR 89:2203-2211

Matroids, Matchings, and Fairness

Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvtiskii ; PMLR 89:2212-2220

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

Wojciech Tarnowski, Piotr Warchoł, Stanisław Jastrzȩbski, Jacek Tabor, Maciej Nowak ; PMLR 89:2221-2230

The Termination Critic

Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup ; PMLR 89:2231-2240

Consistent Online Optimization: Convex and Submodular

Mohammad Reza Karimi Jaghargh, Andreas Krause, Silvio Lattanzi, Sergei Vassilvtiskii ; PMLR 89:2241-2250

Learning Determinantal Point Processes by Corrective Negative Sampling

Zelda Mariet, Mike Gartrell, Suvrit Sra ; PMLR 89:2251-2260

Probabilistic Semantic Inpainting with Pixel Constrained CNNs

Emilien Dupont, Suhas Suresha ; PMLR 89:2261-2270

Least Squares Estimation of Weakly Convex Functions

Sun Sun, Yaoliang Yu ; PMLR 89:2271-2280

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding

Nathan Kallus, Xiaojie Mao, Angela Zhou ; PMLR 89:2281-2290

Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes

Linfeng Liu, Liping Liu ; PMLR 89:2291-2300

Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series

Rui Xie, Zengyan Wang, Shuyang Bai, Ping Ma, Wenxuan Zhong ; PMLR 89:2301-2311

Interpretable Cascade Classifiers with Abstention

Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar ; PMLR 89:2312-2320

Kernel Exponential Family Estimation via Doubly Dual Embedding

Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He ; PMLR 89:2321-2330

Revisiting Adversarial Risk

Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar ; PMLR 89:2331-2339

A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems

Rishabh Iyer, Jeffrey Bilmes ; PMLR 89:2340-2349

Bernoulli Race Particle Filters

Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis ; PMLR 89:2350-2358

Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models

Kaspar Märtens, Michalis Titsias, Christopher Yau ; PMLR 89:2359-2367

Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

Anton Mallasto, Søren Hauberg, Aasa Feragen ; PMLR 89:2368-2377

Unbiased Smoothing using Particle Independent Metropolis-Hastings

Lawrece Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob ; PMLR 89:2378-2387

Two-temperature logistic regression based on the Tsallis divergence

Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan ; PMLR 89:2388-2396

Avoiding Latent Variable Collapse with Generative Skip Models

Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei ; PMLR 89:2397-2405

SMOGS: Social Network Metrics of Game Success

Fan Bu, Sonia Xu, Katherine Heller, Alexander Volfovsky ; PMLR 89:2406-2414

Fast Algorithms for Sparse Reduced-Rank Regression

Benjamin Dubois, Jean-François Delmas, Guillaume Obozinski ; PMLR 89:2415-2424

Modeling simple structures and geometry for better stochastic optimization algorithms

Hilal Asi, John C. Duchi ; PMLR 89:2425-2434

Online learning with feedback graphs and switching costs

Anshuka Rangi, Massimo Franceschetti ; PMLR 89:2435-2444

Interpretable Almost-Exact Matching for Causal Inference

Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky ; PMLR 89:2445-2453

Statistical Optimal Transport via Factored Couplings

Aden Forrow, Jan-Christian Hütter, Mor Nitzan, Philippe Rigollet, Geoffrey Schiebinger, Jonathan Weed ; PMLR 89:2454-2465

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

I (Eli) Chien, Huozhi Zhou, Pan Li ; PMLR 89:2466-2475

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, Pierre E. Jacob, Demba Ba ; PMLR 89:2476-2484

Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods

Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie ; PMLR 89:2485-2494

An Optimal Control Approach to Sequential Machine Teaching

Laurent Lessard, Xuezhou Zhang, Xiaojin Zhu ; PMLR 89:2495-2503

An Online Algorithm for Smoothed Regression and LQR Control

Gautam Goel, Adam Wierman ; PMLR 89:2504-2513

Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization

Aditya Grover, Stefano Ermon ; PMLR 89:2514-2524

Structured Disentangled Representations

Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem Meent ; PMLR 89:2525-2534

Estimating Network Structure from Incomplete Event Data

Benjamin Mark, Garvesh Raskutti, Rebecca Willett ; PMLR 89:2535-2544

Locally Private Mean Estimation: $Z$-test and Tight Confidence Intervals

Marco Gaboardi, Ryan Rogers, Or Sheffet ; PMLR 89:2545-2554

Estimation of Non-Normalized Mixture Models

Takeru Matsuda, Aapo Hyvärinen ; PMLR 89:2555-2563

Rotting bandits are no harder than stochastic ones

Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko ; PMLR 89:2564-2572

A Topological Regularizer for Classifiers via Persistent Homology

Chao Chen, Xiuyan Ni, Qinxun Bai, Yusu Wang ; PMLR 89:2573-2582

Overcomplete Independent Component Analysis via SDP

Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis Bach, Alexandre d’Aspremont, David Sontag ; PMLR 89:2583-2592

Doubly Semi-Implicit Variational Inference

Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov ; PMLR 89:2593-2602

Reducing training time by efficient localized kernel regression

Nicole Müecke ; PMLR 89:2603-2610

Scalable High-Order Gaussian Process Regression

Shandian Zhe, Wei Xing, Robert M. Kirby ; PMLR 89:2611-2620

A Higher-Order Kolmogorov-Smirnov Test

Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani ; PMLR 89:2621-2630

Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference

Kelvin Hsu, Fabio Ramos ; PMLR 89:2631-2640

Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables

Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon ; PMLR 89:2641-2649

Credit Assignment Techniques in Stochastic Computation Graphs

Théophane Weber, Nicolas Heess, Lars Buesing, David Silver ; PMLR 89:2650-2660

Efficient Bayesian Optimization for Target Vector Estimation

Anders Kirk Uhrenholt, Bjøern Sand Jensen ; PMLR 89:2661-2670

Correspondence Analysis Using Neural Networks

Hsiang Hsu, Salman Salamatian, Flavio P. Calmon ; PMLR 89:2671-2680

Interpolating between Optimal Transport and MMD using Sinkhorn Divergences

Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouve, Gabriel Peyré ; PMLR 89:2681-2690

Multi-Observation Regression

Rafael Frongillo, Nishant A. Mehta, Tom Morgan, Bo Waggoner ; PMLR 89:2691-2700

Adaptive MCMC via Combining Local Samplers

Kiárash Shaloudegi, András György ; PMLR 89:2701-2710

Variance reduction properties of the reparameterization trick

Ming Xu, Matias Quiroz, Robert Kohn, Scott A. Sisson ; PMLR 89:2711-2720

Hierarchical Clustering for Euclidean Data

Moses Charikar, Vaggos Chatziafratis, Rad Niazadeh, Grigory Yaroslavtsev ; PMLR 89:2721-2730

Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan ; PMLR 89:2731-2740

Variational Noise-Contrastive Estimation

Benjamin Rhodes, Michael U. Gutmann ; PMLR 89:2741-2750

Improving Quadrature for Constrained Integrands

Henry R. Chai, Roman Garnett ; PMLR 89:2751-2759

High Dimensional Inference in Partially Linear Models

Ying Zhu, Zhuqing Yu, Guang Cheng ; PMLR 89:2760-2769

Cost aware Inference for IoT Devices

Pengkai Zhu, Durmus Alp Emre Acar, Nan Feng, Prateek Jain, Venkatesh Saligrama ; PMLR 89:2770-2779

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era

Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman ; PMLR 89:2780-2789

A Unified Weight Learning Paradigm for Multi-view Learning

Lai Tian, Feiping Nie, Xuelong Li ; PMLR 89:2790-2800

Region-Based Active Learning

Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang ; PMLR 89:2801-2809

Precision Matrix Estimation with Noisy and Missing Data

Roger Fan, Byoungwook Jang, Yuekai Sun, Shuheng Zhou ; PMLR 89:2810-2819

Exploring $k$ out of Top $ρ$ Fraction of Arms in Stochastic Bandits

Wenbo Ren, Jia Liu, Ness B. Shroff ; PMLR 89:2820-2828

AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI

Chen Yu, Bojan Karlaš, Jie Zhong, Ce Zhang, Ji Liu ; PMLR 89:2829-2838

On Theory for BART

Veronika Ročková, Enakshi Saha ; PMLR 89:2839-2848

Deep Topic Models for Multi-label Learning

Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou, Piyush Rai ; PMLR 89:2849-2857

On the Dynamics of Gradient Descent for Autoencoders

Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde ; PMLR 89:2858-2867

Complexities in Projection-Free Stochastic Non-convex Minimization

Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian ; PMLR 89:2868-2876

Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference

Mike Wu, Noah Goodman, Stefano Ermon ; PMLR 89:2877-2886

Efficient Greedy Coordinate Descent for Composite Problems

Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi ; PMLR 89:2887-2896

Decentralized Gradient Tracking for Continuous DR-Submodular Maximization

Jiahao Xie, Chao Zhang, Zebang Shen, Chao Mi, Hui Qian ; PMLR 89:2897-2906

Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models

Craig Kelly, Somdeb Sarkhel, Deepak Venugopal ; PMLR 89:2907-2915

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems

Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter Bartlett, Martin Wainwright ; PMLR 89:2916-2925

Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective

Anirudh Vemula, Wen Sun, J. Bagnell ; PMLR 89:2926-2935

Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics

Difan Zou, Pan Xu, Quanquan Gu ; PMLR 89:2936-2945

Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation

Mingming Sun, Ping Li ; PMLR 89:2946-2955

Imitation-Regularized Offline Learning

Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy ; PMLR 89:2956-2965

A maximum-mean-discrepancy goodness-of-fit test for censored data

Tamara Fernandez, Arthur Gretton ; PMLR 89:2966-2975

Sobolev Descent

Youssef Mroueh, Tom Sercu, Anant Raj ; PMLR 89:2976-2985

Learning the Structure of a Nonstationary Vector Autoregression

Daniel Malinsky, Peter Spirtes ; PMLR 89:2986-2994

Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning

Tadashi Kozuno, Eiji Uchibe, Kenji Doya ; PMLR 89:2995-3003

A Fast Sampling Algorithm for Maximum Inner Product Search

QIN DING, Hsiang-Fu Yu, Cho-Jui Hsieh ; PMLR 89:3004-3012

Minimum Volume Topic Modeling

Byoungwook Jang, Alfred Hero ; PMLR 89:3013-3021

Binary Space Partitioning Forest

Xuhui Fan, Bin Li, Scott SIsson ; PMLR 89:3022-3031

Improved Semi-Supervised Learning with Multiple Graphs

Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi ; PMLR 89:3032-3041

Optimizing over a Restricted Policy Class in MDPs

Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis ; PMLR 89:3042-3050

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate

Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry ; PMLR 89:3051-3059

Deep Switch Networks for Generating Discrete Data and Language

Payam Delgosha, Naveen Goela ; PMLR 89:3060-3069

A recurrent Markov state-space generative model for sequences

Anand Ramachandran, Steve Lumetta, Eric Klee, Deming Chen ; PMLR 89:3070-3079

A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects

Daniel Malinsky, Ilya Shpitser, Thomas Richardson ; PMLR 89:3080-3088

Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators

Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang, Jing Xiao ; PMLR 89:3089-3098

Adaptive Estimation for Approximate $k$-Nearest-Neighbor Computations

Daniel LeJeune, Reinhard Heckel, Richard Baraniuk ; PMLR 89:3099-3107

Model-Free Linear Quadratic Control via Reduction to Expert Prediction

Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari ; PMLR 89:3108-3117

Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport

Adarsh Subbaswamy, Peter Schulam, Suchi Saria ; PMLR 89:3118-3127

Structured Robust Submodular Maximization: Offline and Online Algorithms

Nima Anari, Nika Haghtalab, Seffi Naor, Sebastian Pokutta, Mohit Singh, Alfredo Torrico ; PMLR 89:3128-3137

Sample-Efficient Imitation Learning via Generative Adversarial Nets

Lionel Blondé, Alexandros Kalousis ; PMLR 89:3138-3148

Probabilistic Multilevel Clustering via Composite Transportation Distance

Nhat Ho, Viet Huynh, Dinh Phung, Michael Jordan ; PMLR 89:3149-3157

A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes

Jialin Song, Yuxin Chen, Yisong Yue ; PMLR 89:3158-3167

Online Algorithm for Unsupervised Sensor Selection

Arun Verma, Manjesh Hanawal, Csaba Szepesvari, Venkatesh Saligrama ; PMLR 89:3168-3176

Best of many worlds: Robust model selection for online supervised learning

Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter Bartlett ; PMLR 89:3177-3186

Accelerating Imitation Learning with Predictive Models

Ching-An Cheng, Xinyan Yan, Evangelos Theodorou, Byron Boots ; PMLR 89:3187-3196

Online Learning in Kernelized Markov Decision Processes

Sayak Ray Chowdhury, Aditya Gopalan ; PMLR 89:3197-3205

Lifting high-dimensional non-linear models with Gaussian regressors

Christos Thrampoulidis, Ankit Singh Rawat ; PMLR 89:3206-3215

Domain-Size Aware Markov Logic Networks

Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla ; PMLR 89:3216-3224

Database Alignment with Gaussian Features

Osman E. Dai, Daniel Cullina, Negar Kiyavash ; PMLR 89:3225-3233

Size of Interventional Markov Equivalence Classes in random DAG models

Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler ; PMLR 89:3234-3243

Reparameterizing Distributions on Lie Groups

Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré ; PMLR 89:3244-3253

Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization

Xiangru Lian, Ji Liu ; PMLR 89:3254-3263

Multi-Order Information for Working Set Selection of Sequential Minimal Optimization

Qimao Yang, Changrong Li, Jun Guo ; PMLR 89:3264-3272

Harmonizable mixture kernels with variational Fourier features

Zheyang Shen, Markus Heinonen, Samuel Kaski ; PMLR 89:3273-3282

Multiscale Gaussian Process Level Set Estimation

Shubhanshu Shekhar, Tara Javidi ; PMLR 89:3283-3291

The LORACs Prior for VAEs: Letting the Trees Speak for the Data

Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson ; PMLR 89:3292-3301

Adversarial Learning of a Sampler Based on an Unnormalized Distribution

Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin ; PMLR 89:3302-3311

Active Ranking with Subset-wise Preferences

Aadirupa Saha, Aditya Gopalan ; PMLR 89:3312-3321

Recovery Guarantees For Quadratic Tensors With Sparse Observations

Hongyang Zhang, Vatsal Sharan, Moses Charikar, Yingyu Liang ; PMLR 89:3322-3332

Sample Efficient Graph-Based Optimization with Noisy Observations

Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton ; PMLR 89:3333-3341

Robustness Guarantees for Density Clustering

Heinrich Jiang, Jennifer Jang, Ofir Nachum ; PMLR 89:3342-3351

Fixing Mini-batch Sequences with Hierarchical Robust Partitioning

Shengjie Wang, Wenruo Bai, Chandrashekhar Lavania, Jeff Bilmes ; PMLR 89:3352-3361

Multitask Metric Learning: Theory and Algorithm

Boyu Wang, Hejia Zhang, Peng Liu, Zebang Shen, Joelle Pineau ; PMLR 89:3362-3371

Efficient Bayes Risk Estimation for Cost-Sensitive Classification

Daniel Andrade, Yuzuru Okajima ; PMLR 89:3372-3381

Interpreting Black Box Predictions using Fisher Kernels

Rajiv Khanna, Been Kim, Joydeep Ghosh, Sanmi Koyejo ; PMLR 89:3382-3390

Representation Learning on Graphs: A Reinforcement Learning Application

Sephora Madjiheurem, Laura Toni ; PMLR 89:3391-3399

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

Raj Agrawal, Chandler Squires, Karren Yang, Karthikeyan Shanmugam, Caroline Uhler ; PMLR 89:3400-3409

Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design

Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue ; PMLR 89:3410-3419

Convergence of Gradient Descent on Separable Data

Mor Shpigel Nacson, Jason Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry ; PMLR 89:3420-3428

Structured Neural Topic Models for Reviews

Babak Esmaeili, Hongyi Huang, Byron Wallace, Jan-Willem van de Meent ; PMLR 89:3429-3439

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity

Kohei Miyaguchi, Kenji Yamanishi ; PMLR 89:3440-3448

Low-Dimensional Density Ratio Estimation for Covariate Shift Correction

Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang ; PMLR 89:3449-3458

Evaluating model calibration in classification

Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas Schön ; PMLR 89:3459-3467

Towards Gradient Free and Projection Free Stochastic Optimization

Anit Kumar Sahu, Manzil Zaheer, Soummya Kar ; PMLR 89:3468-3477

On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative

Alexander D’Amour ; PMLR 89:3478-3486

Data-Driven Approach to Multiple-Source Domain Adaptation

Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang ; PMLR 89:3487-3496

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