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Found 7 papers, 2 papers with code
Incentivized Exploration via Filtered Posterior Sampling
We study "incentivized exploration" (IE) in social learning problems where the principal (a recommendation algorithm) can leverage information asymmetry to incentivize sequentially-arriving agents to take exploratory actions.
Information Disclosure and Promotion Policy Design for Platforms
Identifying effective joint information design and promotion policies is a challenging dynamic problem as sellers can sequentially learn the promotion value from sales observations and update prices accordingly.
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.
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards
In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution.
Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with Non-stationary Rewards
In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution.
