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stat.CO2025
Harnessing the Power of Reinforcement Learning for Adaptive MCMC
Congye Wang, Matthew A. Fisher, Heishiro Kanagawa +2
Sampling algorithms drive probabilistic machine learning, and recent years have seen an explosion in the diversity of tools for this task. However, the increasing sophistication of…
stat.CO2024
Reinforcement Learning for Adaptive MCMC
Congye Wang, Wilson Chen, Heishiro Kanagawa +1
An informal observation, made by several authors, is that the adaptive design of a Markov transition kernel has the flavour of a reinforcement learning task. Yet, to-date it has re…
stat.CO2023
Stein -Importance Sampling
Congye Wang, Wilson Chen, Heishiro Kanagawa +1
Stein discrepancies have emerged as a powerful tool for retrospective improvement of Markov chain Monte Carlo output. However, the question of how to design Markov chains that are…