Poster
Thompson Sampling and Approximate Inference
My Phan · Yasin Abbasi Yadkori · Justin Domke
East Exhibition Hall B + C #45
Keywords: [ Probabilistic Methods -> MCMC; Probabilistic Methods ] [ Variational Inference ] [ Algorithms ] [ Bandit Algorithms ]
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Abstract
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Abstract:
We study the effects of approximate inference on the performance of Thompson sampling in the $k$-armed bandit problems. Thompson sampling is a successful algorithm for online decision-making but requires posterior inference, which often must be approximated in practice. We show that even small constant inference error (in $\alpha$-divergence) can lead to poor performance (linear regret) due to under-exploration (for $\alpha<1$) or over-exploration (for $\alpha>0$) by the approximation. While for $\alpha > 0$ this is unavoidable, for $\alpha \leq 0$ the regret can be improved by adding a small amount of forced exploration even when the inference error is a large constant.
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