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Found 3 papers, 1 papers with code
Smoothness-Adaptive Contextual Bandits
In this work, we consider a framework where the smoothness of payoff functions is not known, and study when and how algorithms may adapt to unknown smoothness.
Adaptive Sequential Experiments with Unknown Information Arrival Processes
When it is known how to map auxiliary data to reward estimates, by obtaining matching lower and upper bounds we characterize a spectrum of minimax complexities for this class of problems as a function of the information arrival process, which captures how salient characteristics of this process impact achievable performance.
Adaptive Learning with Unknown Information Flows
We introduce an adaptive exploration policy that, without any prior knowledge of the information arrival process, attains the best performance (in terms of regret rate) that is achievable when the information arrival process is a priori known.
