Volume 117: Algorithmic Learning Theory, , San Diego, California, USA

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Editors: Aryeh Kontorovich, Gergely Neu

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Algorithmic Learning Theory 2020: Preface

Aryeh Kontorovich, Gergely Neu ; PMLR 117:1-2

Optimal multiclass overfitting by sequence reconstruction from Hamming queries

Jayadev Acharya, Ananda Theertha Suresh ; PMLR 117:3-21

Leverage Score Sampling for Faster Accelerated Regression and ERM

Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin-Tat Lee, Praneeth Netrapalli, Aaron Sidford ; PMLR 117:22-47

On Learnability wih Computable Learners

Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner ; PMLR 117:48-60

Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions

Shubhada Agrawal, Sandeep Juneja, Peter Glynn ; PMLR 117:61-110

A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates

Yossi Arjevani, Ohad Shamir, Nathan Srebro ; PMLR 117:111-132

Distribution Free Learning with Local Queries

Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz ; PMLR 117:133-147

Robust Algorithms for Online $k$-means Clustering

Aditya Bhaskara, Aravinda Kanchana Rwanpathirana ; PMLR 117:148-173

What relations are reliably embeddable in Euclidean space?

Robi Bhattacharjee, Sanjoy Dasgupta ; PMLR 117:174-195

First-Order Bayesian Regret Analysis of Thompson Sampling

Sébastien Bubeck, Mark Sellke ; PMLR 117:196-233

Cooperative Online Learning: Keeping your Neighbors Updated

Nicolò Cesa-Bianchi, Tommaso Cesari, Claire Monteleoni ; PMLR 117:234-250

Cautious Limit Learning

Vanja Doskoč, Timo Kötzing ; PMLR 117:251-276

Interactive Learning of a Dynamic Structure

Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian, Robert E. Schapire ; PMLR 117:277-296

Sampling Without Compromising Accuracy in Adaptive Data Analysis

Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein ; PMLR 117:297-318

An adaptive stochastic optimization algorithm for resource allocation

Xavier Fontaine, Shie Mannor, Vianney Perchet ; PMLR 117:319-363

Adversarially Robust Learning Could Leverage Computational Hardness.

Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mahmoody Mohammad ; PMLR 117:364-385

Exponentiated Gradient Meets Gradient Descent

Udaya Ghai, Elad Hazan, Yoram Singer ; PMLR 117:386-407

The Nonstochastic Control Problem

Elad Hazan, Sham Kakade, Karan Singh ; PMLR 117:408-421

On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes

Kothari K. Pravesh, Livni Roi ; PMLR 117:422-450

Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop

Dmitry Kovalev, Samuel Horváth, Peter Richtárik ; PMLR 117:451-467

Algebraic and Analytic Approaches for Parameter Learning in Mixture Models

Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal ; PMLR 117:468-489

Robust guarantees for learning an autoregressive filter

Holden Lee, Cyril Zhang ; PMLR 117:490-517

Thompson Sampling for Adversarial Bit Prediction

Yuval Lewi, Haim Kaplan, Yishay Mansour ; PMLR 117:518-553

On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption

Hanti Lin, Jiji Zhang ; PMLR 117:554-582

On the Complexity of Proper Distribution-Free Learning of Linear Classifiers

Philip M. Long, Raphael J. Long ; PMLR 117:583-591

Feedback graph regret bounds for Thompson Sampling and UCB

Thodoris Lykouris, Éva Tardos, Drishti Wali ; PMLR 117:592-614

Toward universal testing of dynamic network models

Abram Magner, Wojciech Szpankowski ; PMLR 117:615-633

On the Analysis of EM for truncated mixtures of two Gaussians

Sai Ganesh Nagarajan, Ioannis Panageas ; PMLR 117:634-659

A Non-Trivial Algorithm Enumerating Relevant Features over Finite Fields

Mikito Nanashima ; PMLR 117:660-686

Privately Answering Classification Queries in the Agnostic PAC Model

Anupama Nandi, Raef Bassily ; PMLR 117:687-703

Efficient Private Algorithms for Learning Large-Margin Halfspaces

Huy Lê Nguyễn, Jonathan Ullman, Lydia Zakynthinou ; PMLR 117:704-724

Finding Robust Nash equilibria

Vianney Perchet ; PMLR 117:725-751

Top-$k$ Combinatorial Bandits with Full-Bandit Feedback

Idan Rejwan, Yishay Mansour ; PMLR 117:752-776

Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions

Charles Riou, Junya Honda ; PMLR 117:777-826

Approximate Representer Theorems in Non-reflexive Banach Spaces

Kevin Schlegel ; PMLR 117:827-844

Online Non-Convex Learning: Following the Perturbed Leader is Optimal

Arun Sai Suggala, Praneeth Netrapalli ; PMLR 117:845-861

Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling

Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes ; PMLR 117:862-889

Mixing Time Estimation in Ergodic Markov Chains from a Single Trajectory with Contraction Methods

Geoffrey Wolfer ; PMLR 117:890-905

Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies

Tom Zahavy, Avinatan Hasidim, Haim Kaplan, Yishay Mansour ; PMLR 117:906-934

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