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Volume 108: International Conference on Artificial Intelligence and Statistics, 26-28 August 2020, Online

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Editors: Silvia Chiappa, Roberto Calandra

[bib][citeproc]

Linearly Convergent Frank-Wolfe with Backtracking Line-Search

Fabian Pedregosa, Geoffrey Negiar, Armin Askari, Martin Jaggi; PMLR 108:1-10

Guarantees of Stochastic Greedy Algorithms for Non-monotone Submodular Maximization with Cardinality Constraint

Shinsaku Sakaue; PMLR 108:11-21

On Maximization of Weakly Modular Functions: Guarantees of Multi-stage Algorithms, Tractability, and Hardness

Shinsaku Sakaue; PMLR 108:22-33

Adaptive Trade-Offs in Off-Policy Learning

Mark Rowland, Will Dabney, Remi Munos; PMLR 108:34-44

Conditional Importance Sampling for Off-Policy Learning

Mark Rowland, Anna Harutyunyan, Hado Hasselt, Diana Borsa, Tom Schaul, Remi Munos, Will Dabney; PMLR 108:45-55

Multiplicative Gaussian Particle Filter

Xuan Su, Wee Sun Lee, Zhen Zhang; PMLR 108:56-65

Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons

Jingyan Wang, Nihar Shah, R Ravi; PMLR 108:66-76

Fast and Accurate Ranking Regression

Ilkay Yildiz, Jennifer Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis; PMLR 108:77-88

Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy

Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar; PMLR 108:89-99

Long-and Short-Term Forecasting for Portfolio Selection with Transaction Costs

Guy Uziel, Ran El-Yaniv; PMLR 108:100-110

Nonparametric Sequential Prediction While Deep Learning the Kernel

Guy Uziel; PMLR 108:111-121

Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation

Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, Lei Li; PMLR 108:122-132

A Double Residual Compression Algorithm for Efficient Distributed Learning

Xiaorui Liu, Yao Li, Jiliang Tang, Ming Yan; PMLR 108:133-143

Asynchronous Gibbs Sampling

Alexander Terenin, Daniel Simpson, David Draper; PMLR 108:144-154

Learning Fair Representations for Kernel Models

Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar; PMLR 108:155-166

A Nonparametric Off-Policy Policy Gradient

Samuele Tosatto, Joao Carvalho, Hany Abdulsamad, Jan Peters; PMLR 108:167-177

Non-Parametric Calibration for Classification

Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel); PMLR 108:178-190

Minimax Testing of Identity to a Reference Ergodic Markov Chain

Geoffrey Wolfer, Aryeh Kontorovich; PMLR 108:191-201

A Linear-time Independence Criterion Based on a Finite Basis Approximation

Longfei Yan, W. Bastiaan Kleijn, Thushara Abhayapala; PMLR 108:202-212

Minimax Bounds for Structured Prediction Based on Factor Graphs

Kevin Bello, Asish Ghoshal, Jean Honorio; PMLR 108:213-222

On the Convergence of SARAH and Beyond

Bingcong Li, Meng Ma, Georgios B. Giannakis; PMLR 108:223-233

Uncertainty in Neural Networks: Approximately Bayesian Ensembling

Tim Pearce, Felix Leibfried, Alexandra Brintrup; PMLR 108:234-244

LIBRE: Learning Interpretable Boolean Rule Ensembles

Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi; PMLR 108:245-255

Marginal Densities, Factor Graph Duality, and High-Temperature Series Expansions

Mehdi Molkaraie; PMLR 108:256-265

Neighborhood Growth Determines Geometric Priors for Relational Representation Learning

Melanie Weber; PMLR 108:266-276

Fair Decisions Despite Imperfect Predictions

Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera; PMLR 108:277-287

A Characterization of Mean Squared Error for Estimator with Bagging

Martin Mihelich, Charles Dognin, Yan Shu, Michael Blot; PMLR 108:288-297

Uncertainty Quantification for Sparse Deep Learning

Yuexi Wang, Veronika Rockova; PMLR 108:298-308

Minimizing Dynamic Regret and Adaptive Regret Simultaneously

Lijun Zhang, Shiyin Lu, Tianbao Yang; PMLR 108:309-319

A Stein Goodness-of-fit Test for Directional Distributions

Wenkai Xu, Takeru Matsuda; PMLR 108:320-330

Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel

Taeeon Park, Taesup Moon; PMLR 108:331-340

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data

Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Andersen; PMLR 108:341-351

Robust Importance Weighting for Covariate Shift

Fengpei Li, Henry Lam, Siddharth Prusty; PMLR 108:352-362

Adaptive Online Kernel Sampling for Vertex Classification

Peng Yang, Ping Li; PMLR 108:363-373

A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning

Nhan Pham, Lam Nguyen, Dzung Phan, PHUONG HA NGUYEN, Marten Dijk, Quoc Tran-Dinh; PMLR 108:374-385

Stopping criterion for active learning based on deterministic generalization bounds

Hideaki Ishibashi, Hideitsu Hino; PMLR 108:386-397

Ivy: Instrumental Variable Synthesis for Causal Inference

Zhaobin Kuang, Frederic Sala, Nimit Sohoni, Sen Wu, Aldo Córdova-Palomera, Jared Dunnmon, James Priest, Christopher Re; PMLR 108:398-410

High Dimensional Robust Sparse Regression

Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis; PMLR 108:411-421

Nested-Wasserstein Self-Imitation Learning for Sequence Generation

Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen, Wenlin Wang, Lawrence Carin; PMLR 108:422-433

Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization

Huang Fang, Zhenan Fan, Yifan Sun, Michael Friedlander; PMLR 108:434-444

Recommendation on a Budget: Column Space Recovery from Partially Observed Entries with Random or Active Sampling

Carolyn Kim, Mohsen Bayati; PMLR 108:445-455

Fast Noise Removal for k-Means Clustering

Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou; PMLR 108:456-466

Sketching Transformed Matrices with Applications to Natural Language Processing

Yingyu Liang, Zhao Song, Mengdi Wang, Lin Yang, Xin Yang; PMLR 108:467-481

Unconditional Coresets for Regularized Loss Minimization

Alireza Samadian, Kirk Pruhs, Benjamin Moseley, Sungjin Im, Ryan Curtin; PMLR 108:482-492

ASAP: Architecture Search, Anneal and Prune

Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik; PMLR 108:493-503

Understanding Generalization in Deep Learning via Tensor Methods

Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang; PMLR 108:504-515

Accelerating Gradient Boosting Machines

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni; PMLR 108:516-526

Online Binary Space Partitioning Forests

Xuhui Fan, Bin Li, Scott SIsson; PMLR 108:527-537

Sparse Hilbert-Schmidt Independence Criterion Regression

Benjamin Poignard, Makoto Yamada; PMLR 108:538-548

Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model

Wasim Huleihel, Ofer Shayevitz; PMLR 108:549-558

Optimal sampling in unbiased active learning

Henrik Imberg, Johan Jonasson, Marina Axelson-Fisk; PMLR 108:559-569

The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure

Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémen\con; PMLR 108:570-579

Diameter-based Interactive Structure Discovery

Christopher Tosh, Daniel Hsu; PMLR 108:580-590

Utility/Privacy Trade-off through the lens of Optimal Transport

Etienne Boursier, Vianney Perchet; PMLR 108:591-601

A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case

Maxime Laborde, Adam Oberman; PMLR 108:602-612

Interpretable Deep Gaussian Processes with Moments

Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao, Patrick Shafto; PMLR 108:613-623

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

Lars Buesing, Nicolas Heess, Theophane Weber; PMLR 108:624-634

Accelerated Bayesian Optimisation through Weight-Prior Tuning

Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Svetha Venkatesh, Laurence Park, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak; PMLR 108:635-645

Variance Reduction for Evolution Strategies via Structured Control Variates

Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir; PMLR 108:646-656

Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning

Zhenzhang Ye, Thomas Möllenhoff, Tao Wu, Daniel Cremers; PMLR 108:657-668

Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization

Kenji Kawaguchi, Haihao Lu; PMLR 108:669-679

A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent

Eduard Gorbunov, Filip Hanzely, Peter Richtarik; PMLR 108:680-690

Entropy Weighted Power k-Means Clustering

Saptarshi Chakraborty, Debolina Paul, Swagatam Das, Jason Xu; PMLR 108:691-701

Identifying and Correcting Label Bias in Machine Learning

Heinrich Jiang, Ofir Nachum; PMLR 108:702-712

AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity

Yibo Zeng, Fei Feng, Wotao Yin; PMLR 108:713-723

Active Community Detection with Maximal Expected Model Change

Dan Kushnir, Benjamin Mirabelli; PMLR 108:724-734

RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders

Takashi Nicholas Maeda, Shohei Shimizu; PMLR 108:735-745

A Simple Approach for Non-stationary Linear Bandits

Peng Zhao, Lijun Zhang, Yuan Jiang, Zhi-Hua Zhou; PMLR 108:746-755

Distributionally Robust Formulation and Model Selection for the Graphical Lasso

Pedro Cisneros-Velarde, Alexander Petersen, Sang-Yun Oh; PMLR 108:756-765

Efficient Spectrum-Revealing CUR Matrix Decomposition

Cheng Chen, Ming Gu, Zhihua Zhang, Weinan Zhang, Yong Yu; PMLR 108:766-775

Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering

Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh; PMLR 108:776-787

Characterization of Overlap in Observational Studies

Michael Oberst, Fredrik Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush Varshney; PMLR 108:788-798

Modular Block-diagonal Curvature Approximations for Feedforward Architectures

Felix Dangel, Stefan Harmeling, Philipp Hennig; PMLR 108:799-808

A Unified Statistically Efficient Estimation Framework for Unnormalized Models

Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda; PMLR 108:809-819

More Powerful Selective Kernel Tests for Feature Selection

Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira; PMLR 108:820-830

Imputation estimators for unnormalized models with missing data

Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim; PMLR 108:831-841

Wasserstein Style Transfer

Youssef Mroueh; PMLR 108:842-852

Elimination of All Bad Local Minima in Deep Learning

Kenji Kawaguchi, Leslie Kaelbling; PMLR 108:853-863

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi; PMLR 108:864-874

Formal Limitations on the Measurement of Mutual Information

David McAllester, Karl Stratos; PMLR 108:875-884

Scalable Feature Selection for (Multitask) Gradient Boosted Trees

Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian; PMLR 108:885-894

Model-Agnostic Counterfactual Explanations for Consequential Decisions

Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera; PMLR 108:895-905

Obfuscation via Information Density Estimation

Hsiang Hsu, Shahab Asoodeh, Flavio Calmon; PMLR 108:906-917

Linear Dynamics: Clustering without identification

Chloe Hsu, Michaela Hardt, Moritz Hardt; PMLR 108:918-929

Low-rank regularization and solution uniqueness in over-parameterized matrix sensing

Kelly Geyer, Anastasios Kyrillidis, Amir Kalev; PMLR 108:930-940

Robustness for Non-Parametric Classification: A Generic Attack and Defense

Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang, Kamalika Chaudhuri; PMLR 108:941-951

Contextual Online False Discovery Rate Control

Shiyun Chen, Shiva Kasiviswanathan; PMLR 108:952-961

Sequential no-Substitution k-Median-Clustering

Tom Hess, Sivan Sabato; PMLR 108:962-972

Robust Learning from Discriminative Feature Feedback

Sanjoy Dasgupta, Sivan Sabato; PMLR 108:973-982

Hermitian matrices for clustering directed graphs: insights and applications

Mihai Cucuringu, Huan Li, He Sun, Luca Zanetti; PMLR 108:983-992

Kernel Conditional Density Operators

Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet; PMLR 108:993-1004

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects

Yao Zhang, Alexis Bellot, Mihaela Schaar; PMLR 108:1005-1014

Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization

Xingchen Ma, Matthew Blaschko; PMLR 108:1015-1025

Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms

Ping Ma, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael Mahoney; PMLR 108:1026-1035

The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions

Feras Saad, Cameron Freer, Martin Rinard, Vikash Mansinghka; PMLR 108:1036-1046

A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization

Zhize Li, Jian LI; PMLR 108:1047-1057

Black Box Submodular Maximization: Discrete and Continuous Settings

Lin Chen, Mingrui Zhang, Hamed Hassani, Amin Karbasi; PMLR 108:1058-1070

Corruption-Tolerant Gaussian Process Bandit Optimization

Ilija Bogunovic, Andreas Krause, Jonathan Scarlett; PMLR 108:1071-1081

On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms

Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar; PMLR 108:1082-1092

Alternating Minimization Converges Super-Linearly for Mixed Linear Regression

Avishek Ghosh, Ramchandran Kannan; PMLR 108:1093-1103

Learning Gaussian Graphical Models via Multiplicative Weights

Anamay Chaturvedi, Jonathan Scarlett; PMLR 108:1104-1114

Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach

Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama; PMLR 108:1115-1125

Infinitely deep neural networks as diffusion processes

Stefano Peluchetti, Stefano Favaro; PMLR 108:1126-1136

Stable behaviour of infinitely wide deep neural networks

Stefano Peluchetti, Stefano Favaro, Sandra Fortini; PMLR 108:1137-1146

Neural Topic Model with Attention for Supervised Learning

Xinyi Wang, YI YANG; PMLR 108:1147-1156

Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method

Pengzhou Wu, Kenji Fukumizu; PMLR 108:1157-1167

Stochastic Bandits with Delay-Dependent Payoffs

Leonardo Cella, Nicoló Cesa-Bianchi; PMLR 108:1168-1177

Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses

Fabien Lauer; PMLR 108:1178-1187

Balancing Learning Speed and Stability in Policy Gradient via Adaptive Exploration

Matteo Papini, Andrea Battistello, Marcello Restelli; PMLR 108:1188-1199

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

Jan Stuehmer, Richard Turner, Sebastian Nowozin; PMLR 108:1200-1210

A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players

Abbas Mehrabian, Etienne Boursier, Emilie Kaufmann, Vianney Perchet; PMLR 108:1211-1221

Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport

François-Pierre Paty, Alexandre d’Aspremont, Marco Cuturi; PMLR 108:1222-1232

On Generalization Bounds of a Family of Recurrent Neural Networks

Minshuo Chen, Xingguo Li, Tuo Zhao; PMLR 108:1233-1243

Simulator Calibration under Covariate Shift with Kernels

Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki; PMLR 108:1244-1253

Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models

Milan Vojnovic, Se-Young Yun, Kaifang Zhou; PMLR 108:1254-1264

A Locally Adaptive Bayesian Cubature Method

Matthew Fisher, Chris Oates, Catherine Powell, Aretha Teckentrup; PMLR 108:1265-1275

Fast and Bayes-consistent nearest neighbors

Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt; PMLR 108:1276-1286

Explaining the Explainer: A First Theoretical Analysis of LIME

Damien Garreau, Ulrike Luxburg; PMLR 108:1287-1296

A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization

Foivos Alimisis, Antonio Orvieto, Gary Becigneul, Aurelien Lucchi; PMLR 108:1297-1307

Deep Active Learning: Unified and Principled Method for Query and Training

Changjian Shui, Fan Zhou, Christian Gagné, Boyu Wang; PMLR 108:1308-1318

Sparse and Low-rank Tensor Estimation via Cubic Sketchings

Botao Hao, Anru R. Zhang, Guang Cheng; PMLR 108:1319-1330

A nonasymptotic law of iterated logarithm for general M-estimators

Nicolas Schreuder, Victor-Emmanuel Brunel, Arnak Dalalyan; PMLR 108:1331-1341

Robust Stackelberg buyers in repeated auctions

Thomas Nedelec, Clement Calauzenes, Vianney Perchet, Noureddine El Karoui; PMLR 108:1342-1351

Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning

Sebastian Farquhar, Michael A. Osborne, Yarin Gal; PMLR 108:1352-1362

Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes

Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang; PMLR 108:1363-1374

Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation

Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji), Mark Schmidt, Simon Lacoste-Julien; PMLR 108:1375-1386

Two-sample Testing Using Deep Learning

Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert; PMLR 108:1387-1398

RATQ: A Universal Fixed-Length Quantizer for Stochastic Optimization

Prathamesh Mayekar, Himanshu Tyagi; PMLR 108:1399-1409

Rep the Set: Neural Networks for Learning Set Representations

Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, Michalis Vazirgiannis; PMLR 108:1410-1420

A Multiclass Classification Approach to Label Ranking

Robin Vogel, Stéphan Clémen\con; PMLR 108:1421-1430

Conservative Exploration in Reinforcement Learning

Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta; PMLR 108:1431-1441

A principled approach for generating adversarial images under non-smooth dissimilarity metrics

Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam Oberman; PMLR 108:1442-1452

Regularization via Structural Label Smoothing

Weizhi Li, Gautam Dasarathy, Visar Berisha; PMLR 108:1453-1463

Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls

Jiacheng Zhuo, Qi Lei, Alex Dimakis, Constantine Caramanis; PMLR 108:1464-1474

Linear Convergence of Adaptive Stochastic Gradient Descent

Yuege Xie, Xiaoxia Wu, Rachel Ward; PMLR 108:1475-1485

Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization

Andi Nika, Sepehr Elahi, Cem Tekin; PMLR 108:1486-1496

A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach

Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil; PMLR 108:1497-1507

Bandit Convex Optimization in Non-stationary Environments

Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou; PMLR 108:1508-1518

Decentralized Multi-player Multi-armed Bandits with No Collision Information

Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang; PMLR 108:1519-1528

Bayesian Image Classification with Deep Convolutional Gaussian Processes

Vincent Dutordoir, Mark Wilk, Artem Artemev, James Hensman; PMLR 108:1529-1539

Optimizing Millions of Hyperparameters by Implicit Differentiation

Jonathan Lorraine, Paul Vicol, David Duvenaud; PMLR 108:1540-1552

A Topology Layer for Machine Learning

Rickard Brüel Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath, Primoz Skraba; PMLR 108:1553-1563

Differentiable Feature Selection by Discrete Relaxation

Rishit Sheth, Nicoló Fusi; PMLR 108:1564-1572

Private Protocols for U-Statistics in the Local Model and Beyond

James Bell, Aurélien Bellet, Adria Gascon, Tejas Kulkarni; PMLR 108:1573-1583

Automatic Differentiation of Some First-Order Methods in Parametric Optimization

Sheheryar Mehmood, Peter Ochs; PMLR 108:1584-1594

DYNOTEARS: Structure Learning from Time-Series Data

Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam; PMLR 108:1595-1605

Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

David Alvarez-Melis, Youssef Mroueh, Tommi Jaakkola; PMLR 108:1606-1617

Competing Bandits in Matching Markets

Lydia T. Liu, Horia Mania, Michael Jordan; PMLR 108:1618-1628

Revisiting the Landscape of Matrix Factorization

Hossein Valavi, Sulin Liu, Peter Ramadge; PMLR 108:1629-1638

Value Preserving State-Action Abstractions

David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael Littman; PMLR 108:1639-1650

GP-VAE: Deep Probabilistic Time Series Imputation

Vincent Fortuin, Dmitry Baranchuk, Gunnar Raetsch, Stephan Mandt; PMLR 108:1651-1661

Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction

Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi; PMLR 108:1662-1672

Optimized Score Transformation for Fair Classification

Dennis Wei, Karthikeyan Natesan Ramamurthy, Flavio Calmon; PMLR 108:1673-1683

Variational Autoencoders for Sparse and Overdispersed Discrete Data

He Zhao, Piyush Rai, Lan Du, Wray Buntine, Dinh Phung, Mingyuan Zhou; PMLR 108:1684-1694

Spatio-temporal alignments: Optimal transport through space and time

Hicham Janati, Marco Cuturi, Alexandre Gramfort; PMLR 108:1695-1704

Accelerating Smooth Games by Manipulating Spectral Shapes

Waïss Azizian, Damien Scieur, Ioannis Mitliagkas, Simon Lacoste-Julien, Gauthier Gidel; PMLR 108:1705-1715

Langevin Monte Carlo without smoothness

Niladri Chatterji, Jelena Diakonikolas, Michael I. Jordan, Peter Bartlett; PMLR 108:1716-1726

EM Converges for a Mixture of Many Linear Regressions

Jeongyeol Kwon, Constantine Caramanis; PMLR 108:1727-1736

Locally Accelerated Conditional Gradients

Jelena Diakonikolas, Alejandro Carderera, Sebastian Pokutta; PMLR 108:1737-1747

Coping With Simulators That Don’t Always Return

Andrew Warrington, Frank Wood, Saeid Naderiparizi; PMLR 108:1748-1758

Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information

Esther Rolf, Michael I. Jordan, Benjamin Recht; PMLR 108:1759-1769

Equalized odds postprocessing under imperfect group information

Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern; PMLR 108:1770-1780

The True Sample Complexity of Identifying Good Arms

Julian Katz-Samuels, Kevin Jamieson; PMLR 108:1781-1791

Validated Variational Inference via Practical Posterior Error Bounds

Jonathan Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick; PMLR 108:1792-1802

A Rule for Gradient Estimator Selection, with an Application to Variational Inference

Tomas Geffner, Justin Domke; PMLR 108:1803-1812

Naive Feature Selection: Sparsity in Naive Bayes

Armin Askari, Alexandre d’Aspremont, Laurent El Ghaoui; PMLR 108:1813-1822

Fixed-confidence guarantees for Bayesian best-arm identification

Xuedong Shang, Rianne Heide, Pierre Menard, Emilie Kaufmann, Michal Valko; PMLR 108:1823-1832

Learning Hierarchical Interactions at Scale: A Convex Optimization Approach

Hussein Hazimeh, Rahul Mazumder; PMLR 108:1833-1843

OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits

Niladri Chatterji, Vidya Muthukumar, Peter Bartlett; PMLR 108:1844-1854

Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning

Andrew Silva, Matthew Gombolay, Taylor Killian, Ivan Jimenez, Sung-Hyun Son; PMLR 108:1855-1865

Sharp Analysis of Expectation-Maximization for Weakly Identifiable Models

Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin Wainwright, Michael Jordan, Bin Yu; PMLR 108:1866-1876

Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory

Jianyi Zhang, Ruiyi Zhang, Lawrence Carin, Changyou Chen; PMLR 108:1877-1887

Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods

Yi Ding, Panos Toulis; PMLR 108:1888-1898

RelatIF: Identifying Explanatory Training Samples via Relative Influence

Elnaz Barshan, Marc-Etienne Brunet, Gintare Karolina Dziugaite; PMLR 108:1899-1909

Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability

Qin Lu, Georgios Karanikolas, Yanning Shen, Georgios B. Giannakis; PMLR 108:1910-1920

Distributionally Robust Bayesian Quadrature Optimization

Thanh Nguyen, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh; PMLR 108:1921-1931

Sparse Orthogonal Variational Inference for Gaussian Processes

Jiaxin Shi, Michalis Titsias, Andriy Mnih; PMLR 108:1932-1942

The Sylvester Graphical Lasso (SyGlasso)

Yu Wang, Byoungwook Jang, Alfred Hero; PMLR 108:1943-1953

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration

Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric; PMLR 108:1954-1964

DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate

Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gurbuzbalaban; PMLR 108:1965-1976

Discrete Action On-Policy Learning with Action-Value Critic

Yuguang Yue, Yunhao Tang, Mingzhang Yin, Mingyuan Zhou; PMLR 108:1977-1987

Old Dog Learns New Tricks: Randomized UCB for Bandit Problems

Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton; PMLR 108:1988-1998

Thompson Sampling for Linearly Constrained Bandits

Vidit Saxena, Joakim Jalden, Joseph Gonzalez; PMLR 108:1999-2009

Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles

Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh; PMLR 108:2010-2020

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani; PMLR 108:2021-2031

Online Learning Using Only Peer Prediction

Yang Liu, Dave Helmbold; PMLR 108:2032-2042

Deontological Ethics By Monotonicity Shape Constraints

Serena Wang, Maya Gupta; PMLR 108:2043-2054

On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis

Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida; PMLR 108:2055-2065

Randomized Exploration in Generalized Linear Bandits

Branislav Kveton, Manzil Zaheer, Csaba Szepesvari, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier; PMLR 108:2066-2076

Assessing Local Generalization Capability in Deep Models

Huan Wang, Nitish Shirish Keskar, Caiming Xiong, Richard Socher; PMLR 108:2077-2087

Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter

Wenshuo Guo, Nhat Ho, Michael Jordan; PMLR 108:2088-2097

Adaptive Discretization for Evaluation of Probabilistic Cost Functions

Christoph Zimmer, Danny Driess, Mona Meister, Nguyen-Tuong Duy; PMLR 108:2098-2108

Censored Quantile Regression Forest

Alexander Hanbo Li, Jelena Bradic; PMLR 108:2109-2119

Choosing the Sample with Lowest Loss makes SGD Robust

Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi; PMLR 108:2120-2130

Learning with minibatch Wasserstein : asymptotic and gradient properties

Kilian Fatras, Younes Zine, Rémi Flamary, Remi Gribonval, Nicolas Courty; PMLR 108:2131-2141

AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC

Ruqi Zhang, A. Feder Cooper, Christopher De Sa; PMLR 108:2142-2152

On casting importance weighted autoencoder to an EM algorithm to learn deep generative models

Dongha Kim, Jaesung Hwang, Yongdai Kim; PMLR 108:2153-2163

Conditional Linear Regression

Diego Calderon, Brendan Juba, Sirui Li, Zongyi Li, Lisa Ruan; PMLR 108:2164-2173

Distributionally Robust Bayesian Optimization

Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause; PMLR 108:2174-2184

On the optimality of kernels for high-dimensional clustering

Leena C Vankadara, Debarghya Ghoshdastidar; PMLR 108:2185-2195

Improved Regret Bounds for Projection-free Bandit Convex Optimization

Dan Garber, Ben Kretzu; PMLR 108:2196-2206

Variational Autoencoders and Nonlinear ICA: A Unifying Framework

Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, Aapo Hyvarinen; PMLR 108:2207-2217

Online Learning with Continuous Variations: Dynamic Regret and Reductions

Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots; PMLR 108:2218-2228

An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss

Shinji Ito; PMLR 108:2229-2239

A Deep Generative Model for Fragment-Based Molecule Generation

Marco Podda, Davide Bacciu, Alessio Micheli; PMLR 108:2240-2250

Deep Structured Mixtures of Gaussian Processes

Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen; PMLR 108:2251-2261

Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization

Lukas Fröhlich, Edgar Klenske, Julia Vinogradska, Christian Daniel, Melanie Zeilinger; PMLR 108:2262-2272

Dependent randomized rounding for clustering and partition systems with knapsack constraints

David Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh; PMLR 108:2273-2283

Domain-Liftability of Relational Marginal Polytopes

Ondrej Kuzelka, Yuyi Wang; PMLR 108:2284-2292

Derivative-Free & Order-Robust Optimisation

Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko; PMLR 108:2293-2303

Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning

Yao Zhang, Daniel Jarrett, Mihaela Schaar; PMLR 108:2304-2314

Dynamic content based ranking

Seppo Virtanen, Mark Girolami; PMLR 108:2315-2324

Fairness Evaluation in Presence of Biased Noisy Labels

Riccardo Fogliato, Alexandra Chouldechova, Max G’Sell; PMLR 108:2325-2336

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification

Han Bao, Masashi Sugiyama; PMLR 108:2337-2347

Decentralized gradient methods: does topology matter?

Giovanni Neglia, Chuan Xu, Don Towsley, Gianmarco Calbi; PMLR 108:2348-2358

Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions

Giorgia Ramponi, Amarildo Likmeta, Alberto Maria Metelli, Andrea Tirinzoni, Marcello Restelli; PMLR 108:2359-2369

Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness

António H. Ribeiro, Koen Tiels, Luis A. Aguirre, Thomas Schön; PMLR 108:2370-2380

Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks

Jinming Xu, Ye Tian, Ying Sun, Gesualdo Scutari; PMLR 108:2381-2391

Stochastic Linear Contextual Bandits with Diverse Contexts

Weiqiang Wu, Jing Yang, Cong Shen; PMLR 108:2392-2401

Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models

Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana; PMLR 108:2402-2412

Balanced Off-Policy Evaluation in General Action Spaces

Arjun Sondhi, David Arbour, Drew Dimmery; PMLR 108:2413-2423

Approximate Cross-Validation in High Dimensions with Guarantees

William Stephenson, Tamara Broderick; PMLR 108:2424-2434

How fine can fine-tuning be? Learning efficient language models

Evani Radiya-Dixit, Xin Wang; PMLR 108:2435-2443

Interpretable Companions for Black-Box Models

Danqing Pan, Tong Wang, Satoshi Hara; PMLR 108:2444-2454

A PTAS for the Bayesian Thresholding Bandit Problem

Yue Qin, Jian Peng, Yuan Zhou; PMLR 108:2455-2464

Learning Rate Adaptation for Differentially Private Learning

Antti Koskela, Antti Honkela; PMLR 108:2465-2475

Thresholding Graph Bandits with GrAPL

Daniel LeJeune, Gautam Dasarathy, Richard Baraniuk; PMLR 108:2476-2485

Bandit optimisation of functions in the Matérn kernel RKHS

David Janz, David Burt, Javier Gonzalez; PMLR 108:2486-2495

Hypothesis Testing Interpretations and Renyi Differential Privacy

Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato; PMLR 108:2496-2506

Lipschitz Continuous Autoencoders in Application to Anomaly Detection

Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik; PMLR 108:2507-2517

Private k-Means Clustering with Stability Assumptions

Moshe Shechner, Or Sheffet, Uri Stemmer; PMLR 108:2518-2528

Momentum in Reinforcement Learning

Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist; PMLR 108:2529-2538

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

Stefan Haller, Mangal Prakash, Lisa Hutschenreiter, Tobias Pietzsch, Carsten Rother, Florian Jug, Paul Swoboda, Bogdan Savchynskyy; PMLR 108:2539-2549

Precision-Recall Curves Using Information Divergence Frontiers

Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly; PMLR 108:2550-2559

Computing Tight Differential Privacy Guarantees Using FFT

Antti Koskela, Joonas Jälkö, Antti Honkela; PMLR 108:2560-2569

Hyperbolic Manifold Regression

Gian Marconi, Carlo Ciliberto, Lorenzo Rosasco; PMLR 108:2570-2580

Approximate Inference with Wasserstein Gradient Flows

Charlie Frogner, Tomaso Poggio; PMLR 108:2581-2590

Thresholding Bandit Problem with Both Duels and Pulls

Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski; PMLR 108:2591-2600

GAIT: A Geometric Approach to Information Theory

Jose Gallego Posada, Ankit Vani, Max Schwarzer, Simon Lacoste-Julien; PMLR 108:2601-2611

On Thompson Sampling for Smoother-than-Lipschitz Bandits

James Grant, David Leslie; PMLR 108:2612-2622

Safe-Bayesian Generalized Linear Regression

Rianne Heide, Alisa Kirichenko, Peter Grunwald, Nishant Mehta; PMLR 108:2623-2633

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, Alejandro Ribeiro, Martin Takac; PMLR 108:2634-2644

Contextual Constrained Learning for Dose-Finding Clinical Trials

Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela Schaar; PMLR 108:2645-2654

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

Mathurin Massias, Quentin Bertrand, Alexandre Gramfort, Joseph Salmon; PMLR 108:2655-2665

Learning Entangled Single-Sample Distributions via Iterative Trimming

Hui Yuan, Yingyu Liang; PMLR 108:2666-2676

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

Travis Moore, Wong Weng-Keen; PMLR 108:2677-2686

Statistical guarantees for local graph clustering

Wooseok Ha, Kimon Fountoulakis, Michael Mahoney; PMLR 108:2687-2697

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

Yuhao Wang, Uma Roy, Caroline Uhler; PMLR 108:2698-2708

On Pruning for Score-Based Bayesian Network Structure Learning

Alvaro Henrique Chaim Correia, James Cussens, Cassio de Campos; PMLR 108:2709-2718

Statistical and Computational Rates in Graph Logistic Regression

Quentin Berthet, Nicolai Baldin; PMLR 108:2719-2730

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

Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne; PMLR 108:2731-2741

Rk-means: Fast Clustering for Relational Data

Ryan Curtin, Benjamin Moseley, Hung Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich; PMLR 108:2742-2752

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

Loucas Pillaud-Vivien, Francis Bach, Tony Lelièvre, Alessandro Rudi, Gabriel Stoltz; PMLR 108:2753-2763

Integrals over Gaussians under Linear Domain Constraints

Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig; PMLR 108:2764-2774

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

Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy; PMLR 108:2775-2785

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

Mathieu Carriere, Frederic Chazal, Yuichi Ike, Theo Lacombe, Martin Royer, Yuhei Umeda; PMLR 108:2786-2796

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

Insu Han, Jennifer Gillenwater; PMLR 108:2797-2807

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

Xubo Yue, Raed AL Kontar; PMLR 108:2808-2818

Robust Optimisation Monte Carlo

Borislav Ikonomov, Michael U. Gutmann; PMLR 108:2819-2829

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis

Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth; PMLR 108:2830-2840

Fast Markov chain Monte Carlo algorithms via Lie groups

Steve Huntsman; PMLR 108:2841-2851

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

Tianyu Li, Bogdan Mazoure, Doina Precup, Guillaume Rabusseau; PMLR 108:2852-2862

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; PMLR 108:2863-2873

Doubly Sparse Variational Gaussian Processes

Vincent Adam, Stefanos Eleftheriadis, Artem Artemev, Nicolas Durrande, James Hensman; PMLR 108:2874-2884

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

Victor Valls, George Iosifidis, Douglas Leith, Leandros Tassiulas; PMLR 108:2885-2895

Persistence Enhanced Graph Neural Network

Qi Zhao, Ze Ye, Chao Chen, Yusu Wang; PMLR 108:2896-2906

Feature relevance quantification in explainable AI: A causal problem

Dominik Janzing, Lenon Minorics, Patrick Bloebaum; PMLR 108:2907-2916

Neural Decomposition: Functional ANOVA with Variational Autoencoders

Kaspar Märtens, Christopher Yau; PMLR 108:2917-2927

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

Kaspar Märtens, Christopher Yau; PMLR 108:2928-2937

How To Backdoor Federated Learning

Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov; PMLR 108:2938-2948

Exploiting Categorical Structure Using Tree-Based Methods

Brian Lucena; PMLR 108:2949-2958

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments

Adam Foster, Martin Jankowiak, Matthew O’Meara, Yee Whye Teh, Tom Rainforth; PMLR 108:2959-2969

Mixed Strategies for Robust Optimization of Unknown Objectives

Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause; PMLR 108:2970-2980

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

Atsushi Nitanda, Taiji Suzuki; PMLR 108:2981-2991

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

Aaron Sidford, Mengdi Wang, Lin Yang, Yinyu Ye; PMLR 108:2992-3002

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

Jonathan Lee, Aldo Pacchiano, Michael Jordan; PMLR 108:3003-3014

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

Gang Wang, Georgios B. Giannakis; PMLR 108:3015-3024

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models

Theo Galy-Fajou, Florian Wenzel, Manfred Opper; PMLR 108:3025-3035

Bayesian Reinforcement Learning via Deep, Sparse Sampling

Divya Grover, Debabrota Basu, Christos Dimitrakakis; PMLR 108:3036-3045

Deterministic Decoding for Discrete Data in Variational Autoencoders

Daniil Polykovskiy, Dmitry Vetrov; PMLR 108:3046-3056

Monotonic Gaussian Process Flows

Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell; PMLR 108:3057-3067

Flexible distribution-free conditional predictive bands using density estimators

Rafael Izbicki, Gilson Shimizu, Rafael Stern; PMLR 108:3068-3077

Variational Integrator Networks for Physically Structured Embeddings

Steindor Saemundsson, Alexander Terenin, Katja Hofmann, Marc Deisenroth; PMLR 108:3078-3087

Black-Box Inference for Non-Linear Latent Force Models

Wil Ward, Tom Ryder, Dennis Prangle, Mauricio Alvarez; PMLR 108:3088-3098

Importance Sampling via Local Sensitivity

Anant Raj, Cameron Musco, Lester Mackey; PMLR 108:3099-3109

Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling

Mojmir Mutny, Michal Derezinski, Andreas Krause; PMLR 108:3110-3120

Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection

Vaggos Chatziafratis, Grigory Yaroslavtsev, Euiwoong Lee, Konstantin Makarychev, Sara Ahmadian, Alessandro Epasto, Mohammad Mahdian; PMLR 108:3121-3132

Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis

Kaige Yang, Laura Toni, Xiaowen Dong; PMLR 108:3133-3143

Enriched mixtures of generalised Gaussian process experts

Charles Gadd, Sara Wade, Alexis Boukouvalas; PMLR 108:3144-3154

Causal Bayesian Optimization

Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González; PMLR 108:3155-3164

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

Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gael Varoquaux; PMLR 108:3165-3174

A Novel Confidence-Based Algorithm for Structured Bandits

Andrea Tirinzoni, Alessandro Lazaric, Marcello Restelli; PMLR 108:3175-3185

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

Quentin Mérigot, Alex Delalande, Frederic Chazal; PMLR 108:3186-3196

Bayesian experimental design using regularized determinantal point processes

Michal Derezinski, Feynman Liang, Michael Mahoney; PMLR 108:3197-3207

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

Giuseppe Di Benedetto, Francois Caron, Yee Whye Teh; PMLR 108:3208-3218

Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation

Sangdon Park, Osbert Bastani, James Weimer, Insup Lee; PMLR 108:3219-3229

Inference of Dynamic Graph Changes for Functional Connectome

Dingjue Ji, Junwei Lu, Yiliang Zhang, Siyuan Gao, Hongyu Zhao; PMLR 108:3230-3240

An approximate KLD based experimental design for models with intractable likelihoods

Ziqiao Ao, Jinglai Li; PMLR 108:3241-3251

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

Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky; PMLR 108:3252-3262

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

Grigory Yaroslavtsev, Samson Zhou, Dmitrii Avdiukhin; PMLR 108:3263-3274

Sample complexity bounds for localized sketching

Rakshith Sharma Srinivasa, Mark Davenport, Justin Romberg; PMLR 108:3275-3284

An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays

Julian Zimmert, Yevgeny Seldin; PMLR 108:3285-3294

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

Zhaozhi Qian, Ahmed Alaa, Alexis Bellot, Mihaela Schaar, Jem Rashbass; PMLR 108:3295-3305

Tensorized Random Projections

Beheshteh Rakhshan, Guillaume Rabusseau; PMLR 108:3306-3316

Nonparametric Estimation in the Dynamic Bradley-Terry Model

Heejong Bong, Wanshan Li, Shamindra Shrotriya, Alessandro Rinaldo; PMLR 108:3317-3326

Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency

Ziv Goldfeld, Kristjan Greenewald; PMLR 108:3327-3337

Learning in Gated Neural Networks

Ashok Makkuva, Sewoong Oh, Sreeram Kannan, Pramod Viswanath; PMLR 108:3338-3348

Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

Niccolo Dalmasso, Ann Lee, Rafael Izbicki, Taylor Pospisil, Ilmun Kim, Chieh-An Lin; PMLR 108:3349-3361

Fenchel Lifted Networks: A Lagrange Relaxation of Neural Network Training

Fangda Gu, Armin Askari, Laurent El Ghaoui; PMLR 108:3362-3371

Adversarial Robustness Guarantees for Classification with Gaussian Processes

Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska, Stephen Roberts; PMLR 108:3372-3382

Causal inference in degenerate systems: An impossibility result

Yue Wang, Linbo Wang; PMLR 108:3383-3392

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing; PMLR 108:3393-3403

Local Differential Privacy for Sampling

Hisham Husain, Borja Balle, Zac Cranko, Richard Nock; PMLR 108:3404-3413

Learning Sparse Nonparametric DAGs

Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric Xing; PMLR 108:3414-3425

Minimax Rank-$1$ Matrix Factorization

Venkatesh Saligrama, Alexander Olshevsky, Julien Hendrickx; PMLR 108:3426-3436

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

Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi; PMLR 108:3437-3449

Data Generation for Neural Programming by Example

Judith Clymo, Haik Manukian, Nathanael Fijalkow, Adria Gascon, Brooks Paige; PMLR 108:3450-3459

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

Yunfeng Cai, Ping Li; PMLR 108:3460-3470

The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits

Ronshee Chawla, Abishek Sankararaman, Ayalvadi Ganesh, Sanjay Shakkottai; PMLR 108:3471-3481

Understanding the Effects of Batching in Online Active Learning

Kareem Amin, Corinna Cortes, Giulia DeSalvo, Afshin Rostamizadeh; PMLR 108:3482-3492

Adaptive multi-fidelity optimization with fast learning rates

Côme Fiegel, Victor Gabillon, Michal Valko; PMLR 108:3493-3502

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, Nicolas Le Roux; PMLR 108:3503-3513

A Reduction from Reinforcement Learning to No-Regret Online Learning

Ching-An Cheng, Remi Tachet Combes, Byron Boots, Geoff Gordon; PMLR 108:3514-3524

The Implicit Regularization of Ordinary Least Squares Ensembles

Daniel LeJeune, Hamid Javadi, Richard Baraniuk; PMLR 108:3525-3535

Adaptive Exploration in Linear Contextual Bandit

Botao Hao, Tor Lattimore, Csaba Szepesvari; PMLR 108:3536-3545

A Three Sample Hypothesis Test for Evaluating Generative Models

Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta; PMLR 108:3546-3556

Learning Ising and Potts Models with Latent Variables

Surbhi Goel; PMLR 108:3557-3566

Learning piecewise Lipschitz functions in changing environments

Dravyansh Sharma, Maria-Florina Balcan, Travis Dick; PMLR 108:3567-3577

POPCORN: Partially Observed Prediction Constrained Reinforcement Learning

Joseph Futoma, Michael Hughes, Finale Doshi-Velez; PMLR 108:3578-3588

Optimal Approximation of Doubly Stochastic Matrices

Nikitas Rontsis, Paul Goulart; PMLR 108:3589-3598

The Expressive Power of a Class of Normalizing Flow Models

Zhifeng Kong, Kamalika Chaudhuri; PMLR 108:3599-3609

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

Grégoire Mialon, Julien Mairal, Alexandre d’Aspremont; PMLR 108:3610-3620

An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise

Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba; PMLR 108:3621-3631

Amortized Inference of Variational Bounds for Learning Noisy-OR

Yiming Yan, Melissa Ailem, Fei Sha; PMLR 108:3632-3641

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

Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco, Alessandro Rudi; PMLR 108:3642-3652

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

Constantinos Daskalakis, Nishanth Dikkala, Ioannis Panageas; PMLR 108:3653-3663

An Asymptotic Rate for the LASSO Loss

Cynthia Rush; PMLR 108:3664-3673

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

Grzegorz Gluch, Rüdiger Urbanke; PMLR 108:3674-3684

Distributed, partially collapsed MCMC for Bayesian Nonparametrics

Kumar Avinava Dubey, Michael Zhang, Eric Xing, Sinead Williamson; PMLR 108:3685-3695

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

Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi; PMLR 108:3696-3706

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

P Sharoff, Nishant Mehta, Ravi Ganti; PMLR 108:3707-3716

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

Hossein Esfandiari, MohammadTaghi Hajiaghayi, Brendan Lucier, Michael Mitzenmacher; PMLR 108:3717-3727

A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Ziyu Wang, Shuyu Cheng, Li Yueru, Jun Zhu, Bo Zhang; PMLR 108:3728-3738

Sharp Asymptotics and Optimal Performance for Inference in Binary Models

Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis; PMLR 108:3739-3749

A Theoretical Case Study of Structured Variational Inference for Community Detection

Mingzhang Yin, Y. X. Rachel Wang, Purnamrita Sarkar; PMLR 108:3750-3761

Orthogonal Gradient Descent for Continual Learning

Mehrdad Farajtabar, Navid Azizan, Alex Mott, Ang Li; PMLR 108:3762-3773

Hamiltonian Monte Carlo Swindles

Dan Piponi, Matthew Hoffman, Pavel Sountsov; PMLR 108:3774-3783

A single algorithm for both restless and rested rotting bandits

Julien Seznec, Pierre Menard, Alessandro Lazaric, Michal Valko; PMLR 108:3784-3794

Adversarial Robustness of Flow-Based Generative Models

Phillip Pope, Yogesh Balaji, Soheil Feizi; PMLR 108:3795-3805

The Power of Batching in Multiple Hypothesis Testing

Tijana Zrnic, Daniel Jiang, Aaditya Ramdas, Michael Jordan; PMLR 108:3806-3815

Adversarial Risk Bounds through Sparsity based Compression

Emilio Balda, Niklas Koep, Arash Behboodi, Rudolf Mathar; PMLR 108:3816-3825

Learning spectrograms with convolutional spectral kernels

Zheyang Shen, Markus Heinonen, Samuel Kaski; PMLR 108:3826-3836

Federated Heavy Hitters Discovery with Differential Privacy

Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li; PMLR 108:3837-3847

Online Batch Decision-Making with High-Dimensional Covariates

Chi-Hua Wang, Guang Cheng; PMLR 108:3848-3857

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

Osbert Bastani; PMLR 108:3858-3869

Scalable Gradients for Stochastic Differential Equations

Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud; PMLR 108:3870-3882

Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans; PMLR 108:3883-3893

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

Zepeng Huo, Arash PakBin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi; PMLR 108:3894-3904

Learnable Bernoulli Dropout for Bayesian Deep Learning

Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh, Mingyuan Zhou, Xiaoning Qian; PMLR 108:3905-3916

General Identification of Dynamic Treatment Regimes Under Interference

Eli Sherman, David Arbour, Ilya Shpitser; PMLR 108:3917-3927

Gaussian Sketching yields a J-L Lemma in RKHS

Samory Kpotufe, Bharath Sriperumbudur; PMLR 108:3928-3937

Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks

Alexander Levine, Soheil Feizi; PMLR 108:3938-3947

Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning

Ming Yin, Yu-Xiang Wang; PMLR 108:3948-3958

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

Zhengjue Wang, Chaojie Wang, Hao Zhang, Zhibin Duan, Mingyuan Zhou, Bo Chen; PMLR 108:3959-3969

Differentiable Causal Backdoor Discovery

Limor Gultchin, Matt Kusner, Varun Kanade, Ricardo Silva; PMLR 108:3970-3979

Stochastic Recursive Variance-Reduced Cubic Regularization Methods

Dongruo Zhou, Quanquan Gu; PMLR 108:3980-3990

Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer

Yanshuai Cao, Peng Xu; PMLR 108:3991-4001

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

Bryan Andrews, Peter Spirtes, Gregory F. Cooper; PMLR 108:4002-4011

One Sample Stochastic Frank-Wolfe

Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi; PMLR 108:4012-4023

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

Tolga Ergen, Mert Pilanci; PMLR 108:4024-4033

A Robust Univariate Mean Estimator is All You Need

Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar; PMLR 108:4034-4044

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

Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang, Corey Chivers, Michael Draugelis, Kai Li, Barbara Engelhardt; PMLR 108:4045-4055

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

Simao Eduardo, Alfredo Nazabal, Christopher K. I. Williams, Charles Sutton; PMLR 108:4056-4066

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; PMLR 108:4067-4077

A Diversity-aware Model for Majority Vote Ensemble Accuracy

Bob Durrant, Nick Lim; PMLR 108:4078-4087

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Amir Zandieh, Navid Nouri, Ameya Velingker, Michael Kapralov, Ilya Razenshteyn; PMLR 108:4088-4097

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

Daniel Bernstein, Basil Saeed, Chandler Squires, Caroline Uhler; PMLR 108:4098-4108

Budget Learning via Bracketing

Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama; PMLR 108:4109-4119

Optimal Algorithms for Multiplayer Multi-Armed Bandits

PO-AN WANG, Alexandre Proutiere, Kaito Ariu, Yassir Jedra, Alessio Russo; PMLR 108:4120-4129

AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning

Rizal Fathony, Zico Kolter; PMLR 108:4130-4140

Optimal Deterministic Coresets for Ridge Regression

Praneeth Kacham, David Woodruff; PMLR 108:4141-4150

Expressiveness and Learning of Hidden Quantum Markov Models

Sandesh Adhikary, Siddarth Srinivasan, Geoff Gordon, Byron Boots; PMLR 108:4151-4161

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

Yunfeng Cai, Ping Li; PMLR 108:4162-4172

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

Shuhang Chen, Adithya Devraj, Ana Busic, Sean Meyn; PMLR 108:4173-4183

Stochastic Neural Network with Kronecker Flow

Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville; PMLR 108:4184-4194

Fair Correlation Clustering

Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian; PMLR 108:4195-4205

Towards Competitive N-gram Smoothing

Moein Falahatgar, Mesrob Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati; PMLR 108:4206-4215

Multi-level Gaussian Graphical Models Conditional on Covariates

Gi Bum Kim, Seyoung Kim; PMLR 108:4216-4225

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

Christian Carmona, Geoff Nicholls; PMLR 108:4226-4235

Invertible Generative Modeling using Linear Rational Splines

Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie; PMLR 108:4236-4246

LdSM: Logarithm-depth Streaming Multi-label Decision Trees

Maryam Majzoubi, Anna Choromanska; PMLR 108:4247-4257

Prior-aware Composition Inference for Spectral Topic Models

Moontae Lee, David Bindel, David Mimno; PMLR 108:4258-4268

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

Molei Tao, Tomoki Ohsawa; PMLR 108:4269-4280

Best-item Learning in Random Utility Models with Subset Choices

Aadirupa Saha, Aditya Gopalan; PMLR 108:4281-4291

Regularized Autoencoders via Relaxed Injective Probability Flow

Abhishek Kumar, Ben Poole, Kevin Murphy; PMLR 108:4292-4301

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

Cheolmin Kim, Diego Klabjan; PMLR 108:4302-4312

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

Mingchen Li, Mahdi Soltanolkotabi, Samet Oymak; PMLR 108:4313-4324

Scalable Nonparametric Factorization for High-Order Interaction Events

Zhimeng Pan, Zheng Wang, Shandian Zhe; PMLR 108:4325-4335

Gaussianization Flows

Chenlin Meng, Yang Song, Jiaming Song, Stefano Ermon; PMLR 108:4336-4345

Adaptive, Distribution-Free Prediction Intervals for Deep Networks

Danijel Kivaranovic, Kory D. Johnson, Hannes Leeb; PMLR 108:4346-4356

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare; PMLR 108:4357-4366

Automatic Differentiation of Sketched Regression

Hang Liao, Barak A. Pearlmutter, Vamsi K. Potluru, David P. Woodruff; PMLR 108:4367-4376

Sublinear Optimal Policy Value Estimation in Contextual Bandits

Weihao Kong, Emma Brunskill, Gregory Valiant; PMLR 108:4377-4387

Budget-Constrained Bandits over General Cost and Reward Distributions

Semih Cayci, Atilla Eryilmaz, R Srikant; PMLR 108:4388-4398

Measuring Mutual Information Between All Pairs of Variables in Subquadratic Complexity

Mohsen Ferdosi, Arash Gholamidavoodi, Hosein Mohimani; PMLR 108:4399-4409

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

Omid Sadeghi, Maryam Fazel; PMLR 108:4410-4419

Prediction Focused Topic Models via Feature Selection

Jason Ren, Russell Kunes, Finale Doshi-Velez; PMLR 108:4420-4429

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization

Dongruo Zhou, Yuan Cao, Quanquan Gu; PMLR 108:4430-4440

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

Christian Weilbach, Boyan Beronov, Frank Wood, William Harvey; PMLR 108:4441-4451

Graph Coarsening with Preserved Spectral Properties

Yu Jin, Andreas Loukas, Joseph JaJa; PMLR 108:4452-4462

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

Andrew Ilyas, Emmanouil Zampetakis, Constantinos Daskalakis; PMLR 108:4463-4473

Permutation Invariant Graph Generation via Score-Based Generative Modeling

Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon; PMLR 108:4474-4484

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

Jun Sun, Gang Wang, Georgios B. Giannakis, Qinmin Yang, Zaiyue Yang; PMLR 108:4485-4495

Multi-attribute Bayesian optimization with interactive preference learning

Raul Astudillo, Peter Frazier; PMLR 108:4496-4507

On the Sample Complexity of Learning Sum-Product Networks

Ishaq Aden-Ali, Hassan Ashtiani; PMLR 108:4508-4518

Tighter Theory for Local SGD on Identical and Heterogeneous Data

Ahmed Khaled, Konstantin Mishchenko, Peter Richtarik; PMLR 108:4519-4529

Approximate Cross-validation: Guarantees for Model Assessment and Selection

Ashia Wilson, Maximilian Kasy, Lester Mackey; PMLR 108:4530-4540

On Minimax Optimality of GANs for Robust Mean Estimation

Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu; PMLR 108:4541-4551

Auditing ML Models for Individual Bias and Unfairness

Songkai Xue, Mikhail Yurochkin, Yuekai Sun; PMLR 108:4552-4562

Stein Variational Inference for Discrete Distributions

Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu; PMLR 108:4563-4572

Revisiting Stochastic Extragradient

Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtarik, Yura Malitsky; PMLR 108:4573-4582

A Framework for Sample Efficient Interval Estimation with Control Variates

Shengjia Zhao, Christopher Yeh, Stefano Ermon; PMLR 108:4583-4592

Nonmyopic Gaussian Process Optimization with Macro-Actions

Dmitrii Kharkovskii, Chun Kai Ling, Bryan Kian Hsiang Low; PMLR 108:4593-4604

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