6 papers
Simultaneous Neural Optimal Transport
Milena Gazdieva, Kirill Sokolov, Jiawei Chen +2
Optimal Transport (OT) provides a principled framework for learning transformations between probability distributions from unpaired samples. In many applications, however, a single…
Markovization of Randomized Stopping Times
Kirill Sokolov
We study randomized stopping times for a Markov process, described by a progressively measurable intensity . We prove that every admissible intensity has a Markovian repres…
Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation
Sergei Kholkin, Kirill Sokolov, Dmitry Baranchuk +2
Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods, especially diffusion-based on…
Duality for a Martingale Transport Problem with Moment Constraints
Alexander V. Kolesnikov, Aleksandra Novikova, Kirill Sokolov
We consider a weak martingale optimal transport problem related to the martingale analogue of the Benamou--Brenier formula. In contrast to the classical setting, the second margina…
Variational Entropic Optimal Transport
Roman Dyachenko, Nikita Gushchin, Kirill Sokolov +3
Entropic optimal transport (EOT) in continuous spaces with quadratic cost is a classical tool for solving the domain translation problem. In practice, recent approaches optimize a…
Exponential convergence rate for Iterative Markovian Fitting
Kirill Sokolov, Alexander Korotin
We consider the discrete-time Schrödinger bridge problem on a finite state space. Although it has been known that the Iterative Markovian Fitting (IMF) algorithm converges in Kullb…