6 papers
Improved Bounds for Reward-Agnostic and Reward-Free Exploration
Oran Ridel, Alon Cohen
We study reward-free and reward-agnostic exploration in episodic finite-horizon Markov decision processes (MDPs), where an agent explores an unknown environment without observing e…
Understanding and Mitigating the Impacts of Differentially Private Census Data on State Level Redistricting
Christian Cianfarani, Aloni Cohen
Data from the Decennial Census is published only after applying a disclosure avoidance system (DAS). Data users were shaken by the adoption of differential privacy in the 2020 DAS,…
Protecting the Undeleted in Machine Unlearning
Aloni Cohen, Refael Kohen, Kobbi Nissim +1
Machine unlearning aims to remove specific data points from a trained model, often striving to emulate "perfect retraining", i.e., producing the model that would have been obtained…
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…
Playing Markov Games Without Observing Payoffs
Daniel Ablin, Alon Cohen
Optimization under uncertainty is a fundamental problem in learning and decision-making, particularly in multi-agent systems. Previously, Feldman, Kalai, and Tennenholtz [2010] dem…
Regret Guarantees for Linear Contextual Stochastic Shortest Path
Dor Polikar, Alon Cohen
We define the problem of linear Contextual Stochastic Shortest Path (CSSP), where at the beginning of each episode, the learner observes an adversarially chosen context that determ…