2 citations · 2 across the 20 of their papers we have counts for
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Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback
Orin Levy, Liad Erez, Alon Cohen +1
We present regret minimization algorithms for the contextual multi-armed bandit (CMAB) problem over actions in the presence of delayed feedback, a scenario where loss observati…
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…
Rising Rested MAB with Linear Drift
Omer Amichay, Yishay Mansour
We consider non-stationary multi-arm bandit (MAB) where the expected reward of each action follows a linear function of the number of times we executed the action. Our main result…