101 citations · 173 across the 27 of their papers we have counts for
3 papers · 1 filter
Action Matching: Learning Stochastic Dynamics from Samples
Kirill Neklyudov, Rob Brekelmans, Daniel Severo +1
Learning the continuous dynamics of a system from snapshots of its temporal marginals is a problem which appears throughout natural sciences and machine learning, including in quan…
Variational Representations of Annealing Paths: Bregman Information under Monotonic Embedding
Rob Brekelmans, Frank Nielsen
Markov Chain Monte Carlo methods for sampling from complex distributions and estimating normalization constants often simulate samples from a sequence of intermediate distributions…
Your Policy Regularizer is Secretly an Adversary
Rob Brekelmans, Tim Genewein, Jordi Grau-Moya +4
Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy. In this paper, we show…