3 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
Improved Best-of-Both-Worlds Regret for Bandits with Delayed Feedback
Ofir Schlisselberg, Tal Lancewicki, Peter Auer +1
We study the multi-armed bandit problem with adversarially chosen delays in the Best-of-Both-Worlds (BoBW) framework, which aims to achieve near-optimal performance in both stochas…
Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback
Tal Lancewicki, Yishay Mansour
We study online finite-horizon Markov Decision Processes with adversarially changing loss and aggregate bandit feedback (a.k.a full-bandit). Under this type of feedback, the agent…
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…
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions
Tal Lancewicki, Shahar Segal, Tomer Koren +1
We study the stochastic Multi-Armed Bandit (MAB) problem with random delays in the feedback received by the algorithm. We consider two settings: the reward-dependent delay setting,…