Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization Clément Bénard 1 Brian Staber 1 Sébastien Da Veiga 2 1

Stein thinning is a promising algorithm proposed by Riabiz et al. [2022] for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference.