Jun 10, 2015 · In this paper, we fully characterize the algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet ...
Sep 21, 2016 · These lower bounds show that the performance of Thompson Sampling can be quite sensitive to the choice of input prior, especially when the prior ...
Abstract. The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its the-.
The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties.
This paper fully characterize the Thompson Sampling algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet ...
The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties.
Jul 23, 2023 · In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems.
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Thompson Sampling, one of the oldest heuristics for solving multi-armed bandits, has recently been shown to demonstrate state-of-the-art performance.
This serves to give the algorithm a “warm start” with more accurate prior beliefs on rewards, rather than N(0, 1). It also decreases the sensitivity of the ...
Thompson sampling (TS) has been known for its outstand- ing empirical performance supported by theoretical guaran- tees across various reward models in the ...