2 citations · 2 across the 20 of their papers we have counts for
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Vanilla Policy Optimization Is Both Optimal and Differentially Private for Stochastic Contextual Bandits
Idan Attias, Orin Levy, Alexander Ryabchenko +2
Can vanilla policy optimization explore enough to achieve near-optimal regret in stochastic contextual bandits? We show that standard exponential policy updates driven by offline r…
Near-Optimal Stochastic Linear Bandits with Delay
Ofir Schlisselberg, Mengxiao Zhang, Yishay Mansour
We study stochastic linear bandits with delayed feedback under several delay models and establish near-optimal regret guarantees. Our results identify when delayed linear bandits e…
Cost-Aware Learning
Clara Mohri, Amir Globerson, Haim Kaplan +2
We consider the problem of Cost-Aware Learning, where sampling different components of a finite-sum objective incurs different costs. The objective is to reach a target error while…
Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
Shashaank Aiyer, Yishay Mansour, Shay Moran +2
We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-sensitive generalization of the f…
Collaborating in Multi-Armed Bandits with Strategic Agents
Idan Barnea, Ofir Schlisselberg, Yishay Mansour
We study collaborative learning in multi-agent Bayesian bandit problems, where strategic agents collectively solve the same bandit instance. While multiple agents can accelerate le…
Online Set Learning from Precision and Recall Feedback
Lee Cohen, Yishay Mansour, Shay Moran +1
We consider the problem of learning an unknown subset of a domain in an online setting. In each round , the learner predicts a set of items and receive…
Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
Christoph Dann, Yishay Mansour, Mehryar Mohri
Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss. However, powerful RL optimizers inevitably exploit minor model inaccura…
Robust Temporal Guarantees in Budgeted Sequential Auctions
Giannis Fikioris, Robert Kleinberg, Yoav Kolumbus +2
In modern advertising platforms, learning algorithms are deployed by budget-constrained bidders to maximize their accumulated value. These algorithms often offer classical utility…
Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
Orin Levy, Aviv Rosenberg, Alon Cohen +1
We introduce \texttt{OPO-CMDP}, the first policy optimization algorithm for stochastic Contextual Markov Decision Process (CMDPs) under general offline function approximation. Our…
Optimal Regret for Policy Optimization in Contextual Bandits
Orin Levy, Yishay Mansour
We present the first high-probability optimal regret bound for a policy optimization technique applied to the problem of stochastic contextual multi-armed bandit (CMAB) with genera…
Online Learning in MDPs with Partially Adversarial Transitions and Losses
Ofir Schlisselberg, Tal Lancewicki, Yishay Mansour
We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of steps per episode. This model ca…
The Horizon Threshold in Cooperative Multi-Agent Reward-Free Exploration
Idan Barnea, Orin Levy, Yishay Mansour
We study cooperative multi-agent reinforcement learning in the setting of reward-free exploration, where multiple agents jointly explore an unknown MDP in order to learn its dynami…
Stochastic Linear Bandits with Parameter Noise
Daniel Ezer, Alon Peled-Cohen, Yishay Mansour
We study the stochastic linear bandits with parameter noise model, in which the reward of action is where is sampled i.i.d. We show a regret upper bound of $\wid…