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Found 5 papers, 0 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.
Complexity Analysis of a Countable-armed Bandit Problem
We also show that the instance-independent (minimax) regret is $\tilde{\mathcal{O}}\left( \sqrt{n} \right)$ when $K=2$.
Bandits with Dynamic Arm-acquisition Costs
We consider a bandit problem where at any time, the decision maker can add new arms to her consideration set.
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms
One of the key drivers of complexity in the classical (stochastic) multi-armed bandit (MAB) problem is the difference between mean rewards in the top two arms, also known as the instance gap.
From Finite to Countable-Armed Bandits
We consider a stochastic bandit problem with countably many arms that belong to a finite set of types, each characterized by a unique mean reward.
