1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchLihong Lihttps://www.microsoft.com/en-us/research/people/lihongli/On the Prior Sensitivity of Thompson Sampling - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="U9KCwE0dSo"><a href="https://www.microsoft.com/en-us/research/publication/prior-sensitivity-thompson-sampling/">On the Prior Sensitivity of Thompson Sampling</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/prior-sensitivity-thompson-sampling/embed/#?secret=U9KCwE0dSo" width="600" height="338" title="“On the Prior Sensitivity of Thompson Sampling” — Microsoft Research" data-secret="U9KCwE0dSo" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more effectively. While it is generally believed that the algorithm’s regret is […]