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• W2968387512 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 $alpha 0$) 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." @default.
• W2968387512 created "2019-08-22" @default.
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• W2968387512 date "2019-08-14" @default.
• W2968387512 modified "2023-09-27" @default.
• W2968387512 title "Thompson Sampling with Approximate Inference" @default.
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