Transformed Samplers with Variance Reduction
Markov chain Monte Carlo (MCMC) methods are the standard tool for computing expectations under complex probability distributions. Control variates reduce the variance of the resulting estimates, but a good control variate requires solving the Poisson equation of the sampler, which rarely admits a closed-form solution. Exact solutions are available when the sampler's kernel has a known spectral decomposition on a simple reference density. In our work, we extend these solutions to general targets through a learned change of variables. A bijection, such as a normalizing flow, is trained so that the target becomes close to the reference in a latent space, and we show that Markov kernels and t...
| Authors | Siran Liu, Michalis Tisias, Petros Dellaportas |
|---|---|
| Primary category | stat.ML |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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