2 citations · 2 across the 20 of their papers we have counts for
4 papers · 1 filter
Individual Regret in Cooperative Stochastic Multi-Armed Bandits
Idan Barnea, Tal Lancewicki, Yishay Mansour
We study the regret in stochastic Multi-Armed Bandits (MAB) with multiple agents that communicate over an arbitrary connected communication graph. We analyzed a variant of Cooperat…
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
Asaf Cassel, Orin Levy, Yishay Mansour
Efficiently trading off exploration and exploitation is one of the key challenges in online Reinforcement Learning (RL). Most works achieve this by carefully estimating the model u…
Delay as Payoff in MAB
Ofir Schlisselberg, Ido Cohen, Tal Lancewicki +1
In this paper, we investigate a variant of the classical stochastic Multi-armed Bandit (MAB) problem, where the payoff received by an agent (either cost or reward) is both delayed,…
Non-stochastic Bandits With Evolving Observations
Yogev Bar-On, Yishay Mansour
We introduce a novel online learning framework that unifies and generalizes pre-established models, such as delayed and corrupted feedback, to encompass adversarial environments wh…