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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
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Optimal multiclass overfitting by sequence reconstruction from Hamming queries
Jayadev Acharya, Ananda Theertha Suresh;
PMLR 117:3-21
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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
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On Learnability wih Computable Learners
Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner;
PMLR 117:48-60
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Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions
Shubhada Agrawal, Sandeep Juneja, Peter Glynn;
PMLR 117:61-110
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A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates
Yossi Arjevani, Ohad Shamir, Nathan Srebro;
PMLR 117:111-132
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Distribution Free Learning with Local Queries
Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz;
PMLR 117:133-147
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Robust Algorithms for Online $k$-means Clustering
Aditya Bhaskara, Aravinda Kanchana Rwanpathirana;
PMLR 117:148-173
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What relations are reliably embeddable in Euclidean space?
Robi Bhattacharjee, Sanjoy Dasgupta;
PMLR 117:174-195
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First-Order Bayesian Regret Analysis of Thompson Sampling
Sébastien Bubeck, Mark Sellke;
PMLR 117:196-233
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Cooperative Online Learning: Keeping your Neighbors Updated
Nicolò Cesa-Bianchi, Tommaso Cesari, Claire Monteleoni;
PMLR 117:234-250
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Cautious Limit Learning
Vanja Doskoč, Timo Kötzing;
PMLR 117:251-276
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Interactive Learning of a Dynamic Structure
Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian, Robert E. Schapire;
PMLR 117:277-296
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Sampling Without Compromising Accuracy in Adaptive Data Analysis
Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein;
PMLR 117:297-318
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An adaptive stochastic optimization algorithm for resource allocation
Xavier Fontaine, Shie Mannor, Vianney Perchet;
PMLR 117:319-363
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Adversarially Robust Learning Could Leverage Computational Hardness.
Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mahmoody Mohammad;
PMLR 117:364-385
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Exponentiated Gradient Meets Gradient Descent
Udaya Ghai, Elad Hazan, Yoram Singer;
PMLR 117:386-407
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The Nonstochastic Control Problem
Elad Hazan, Sham Kakade, Karan Singh;
PMLR 117:408-421
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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
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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
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Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal;
PMLR 117:468-489
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Robust guarantees for learning an autoregressive filter
Holden Lee, Cyril Zhang;
PMLR 117:490-517
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Thompson Sampling for Adversarial Bit Prediction
Yuval Lewi, Haim Kaplan, Yishay Mansour;
PMLR 117:518-553
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On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption
Hanti Lin, Jiji Zhang;
PMLR 117:554-582
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On the Complexity of Proper Distribution-Free Learning of Linear Classifiers
Philip M. Long, Raphael J. Long;
PMLR 117:583-591
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Feedback graph regret bounds for Thompson Sampling and UCB
Thodoris Lykouris, Éva Tardos, Drishti Wali;
PMLR 117:592-614
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Toward universal testing of dynamic network models
Abram Magner, Wojciech Szpankowski;
PMLR 117:615-633
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On the Analysis of EM for truncated mixtures of two Gaussians
Sai Ganesh Nagarajan, Ioannis Panageas;
PMLR 117:634-659
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A Non-Trivial Algorithm Enumerating Relevant Features over Finite Fields
Mikito Nanashima;
PMLR 117:660-686
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Privately Answering Classification Queries in the Agnostic PAC Model
Anupama Nandi, Raef Bassily;
PMLR 117:687-703
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Efficient Private Algorithms for Learning Large-Margin Halfspaces
Huy Lê Nguyễn, Jonathan Ullman, Lydia Zakynthinou;
PMLR 117:704-724
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Finding Robust Nash equilibria
Vianney Perchet;
PMLR 117:725-751
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Top-$k$ Combinatorial Bandits with Full-Bandit Feedback
Idan Rejwan, Yishay Mansour;
PMLR 117:752-776
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Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions
Charles Riou, Junya Honda;
PMLR 117:777-826
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Approximate Representer Theorems in Non-reflexive Banach Spaces
Kevin Schlegel;
PMLR 117:827-844
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Online Non-Convex Learning: Following the Perturbed Leader is Optimal
Arun Sai Suggala, Praneeth Netrapalli;
PMLR 117:845-861
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Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes;
PMLR 117:862-889
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Mixing Time Estimation in Ergodic Markov Chains from a Single Trajectory with Contraction Methods
Geoffrey Wolfer;
PMLR 117:890-905
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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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