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cs.LG2026
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
Kirill Korolev, Nikita Morozov, Stepan Pavlenko +2
Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling. While Markov chain Monte Carlo (MCMC) methods…
cs.LG2026
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…
cs.LG2026
Learning Shortest Paths with Generative Flow Networks
Nikita Morozov, Ian Maksimov, Daniil Tiapkin +1
In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we examine theoretical properties o…