4 papers · 1 filter
Your GFlowNet Secretly Learns an Optimal Transport Plan
Ian Maksimov, Nikita Morozov, Denis Belomestny +1
Generative Flow Networks (GFlowNets) are a framework for sampling structured objects via stochastic trajectories in a directed graph. In this work, we establish a theoretical conne…
Tight Bounds for Schrödinger Potential Estimation in Unpaired Data Translation
Nikita Puchkin, Denis Suchkov, Alexey Naumov +1
Modern methods of generative modelling and unpaired data translation based on Schrödinger bridges and stochastic optimal control theory aim to transform an initial density to a tar…
Sample complexity of Schrödinger potential estimation
Nikita Puchkin, Iurii Pustovalov, Yuri Sapronov +3
We address the problem of Schrödinger potential estimation, which plays a crucial role in modern generative modelling approaches based on Schrödinger bridges and stochastic optimal…
UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms
Denis Belomestny, Ilya Levin, Alexey Naumov +1
Policy evaluation is an important instrument for the comparison of different algorithms in Reinforcement Learning (RL). However, even a precise knowledge of the value function $V^π…