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ORCID31st ALT 2020: San Diego, CA, USA
- Aryeh Kontorovich, Gergely Neu:
Algorithmic Learning Theory, ALT 2020, 8-11 February 2020, San Diego, CA, USA. Proceedings of Machine Learning Research 117, PMLR 2020 - Aryeh Kontorovich, Gergely Neu:
Algorithmic Learning Theory 2020: Preface. 1-2 - Jayadev Acharya, Ananda Theertha Suresh:
Optimal multiclass overfitting by sequence reconstruction from Hamming queries. 3-21 - Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford:
Leverage Score Sampling for Faster Accelerated Regression and ERM. 22-47 - Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
On Learnability wih Computable Learners. 48-60 - Shubhada Agrawal, Sandeep Juneja, Peter W. Glynn:
Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions. 61-110 - Yossi Arjevani, Ohad Shamir, Nathan Srebro:
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. 111-132 - Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz:
Distribution Free Learning with Local Queries. 133-147 - Aditya Bhaskara, Aravinda Kanchana Ruwanpathirana:
Robust Algorithms for Online k-means Clustering. 148-173 - Robi Bhattacharjee, Sanjoy Dasgupta:
What relations are reliably embeddable in Euclidean space? 174-195 - Sébastien Bubeck, Mark Sellke:
First-Order Bayesian Regret Analysis of Thompson Sampling. 196-233 - Nicolò Cesa-Bianchi, Tommaso Cesari, Claire Monteleoni:
Cooperative Online Learning: Keeping your Neighbors Updated. 234-250 - Vanja Doskoc, Timo Kötzing:
Cautious Limit Learning. 251-276 - Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian, Robert E. Schapire:
Interactive Learning of a Dynamic Structure. 277-296 - Benjamin Fish, Lev Reyzin, Benjamin I. P. Rubinstein:
Sampling Without Compromising Accuracy in Adaptive Data Analysis. 297-318 - Xavier Fontaine, Shie Mannor, Vianney Perchet:
An adaptive stochastic optimization algorithm for resource allocation. 319-363 - Sanjam Garg
, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody:
Adversarially Robust Learning Could Leverage Computational Hardness. 364-385 - Udaya Ghai, Elad Hazan, Yoram Singer:
Exponentiated Gradient Meets Gradient Descent. 386-407 - Elad Hazan, Sham M. Kakade, Karan Singh:
The Nonstochastic Control Problem. 408-421 - Pravesh K. Kothari, Roi Livni:
On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes. 422-450 - Dmitry Kovalev, Samuel Horváth, Peter Richtárik:
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. 451-467 - Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal:
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. 468-489 - Holden Lee, Cyril Zhang:
Robust guarantees for learning an autoregressive filter. 490-517 - Yuval Lewi, Haim Kaplan, Yishay Mansour:
Thompson Sampling for Adversarial Bit Prediction. 518-553 - Hanti Lin, Jiji Zhang:
On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption. 554-582 - Philip M. Long, Raphael J. Long:
On the Complexity of Proper Distribution-Free Learning of Linear Classifiers. 583-591 - Thodoris Lykouris, Éva Tardos, Drishti Wali:
Feedback graph regret bounds for Thompson Sampling and UCB. 592-614 - Abram Magner, Wojciech Szpankowski:
Toward universal testing of dynamic network models. 615-633 - Sai Ganesh Nagarajan, Ioannis Panageas:
On the Analysis of EM for truncated mixtures of two Gaussians. 634-659 - Mikito Nanashima:
A Non-Trivial Algorithm Enumerating Relevant Features over Finite Fields. 660-686 - Anupama Nandi, Raef Bassily:
Privately Answering Classification Queries in the Agnostic PAC Model. 687-703 - Huy Le Nguyen, Jonathan R. Ullman, Lydia Zakynthinou:
Efficient Private Algorithms for Learning Large-Margin Halfspaces. 704-724 - Vianney Perchet:
Finding Robust Nash equilibria. 725-751 - Idan Rejwan, Yishay Mansour:
Top-k Combinatorial Bandits with Full-Bandit Feedback. 752-776 - Charles Riou, Junya Honda:
Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions. 777-826 - Kevin Schlegel:
Approximate Representer Theorems in Non-reflexive Banach Spaces. 827-844 - Arun Sai Suggala, Praneeth Netrapalli:
Online Non-Convex Learning: Following the Perturbed Leader is Optimal. 845-861 - Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes:
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling. 862-889 - Geoffrey Wolfer:
Mixing Time Estimation in Ergodic Markov Chains from a Single Trajectory with Contraction Methods. 890-905 - Tom Zahavy, Avinatan Hassidim, Haim Kaplan, Yishay Mansour:
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies. 906-934
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