7 citations · 31 across the 15 of their papers we have counts for
Showing 2021 · stat.MLShow all
2 papers · 2 filters
stat.ML2021★ 4 cited
Local-Global MCMC kernels: the best of both worlds
Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié +3
Recent works leveraging learning to enhance sampling have shown promising results, in particular by designing effective non-local moves and global proposals. However, learning accu…
stat.ML2021★ 4 cited
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling p…