17 papers
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…
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…
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 c…
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…
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 $\w…
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…