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On the Prior Sensitivity of Thompson Sampling
The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties.
Most Correlated Arms Identification
We study the problem of finding the most mutually correlated arms among many arms.
Prior-free and prior-dependent regret bounds for Thompson Sampling
Building on the techniques of Audibert and Bubeck [2009] and Russo and Roy [2013] we first show that Thompson Sampling attains an optimal prior-free bound in the sense that for any prior distribution its Bayesian regret is bounded from above by $14 \sqrt{n K}$.
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