Author

Kirill Tamogashev

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#machine learning Oct 2025

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants

This paper proposes a novel flow matching method that overcomes the limitations of existing multi-marginal trajectory inference algorithms, using a GAN-inspired adversarial loss to fit neurally parametrised interpolant curves between source and target points such that the marginal distributions at intermediate time poi...

Oskar Kviman, Kirill Tamogashev, Nicola Branchini et al. · arXiv.org · 2 citations

#machine learning Sep 2025

Data-to-Energy Stochastic Dynamics

This paper proposes the first general method for modelling Schr\"odinger bridges when one (or both) distributions are given by their unnormalised densities, with no access to data samples, and applies the newly developed algorithm to the problem of sampling posterior distributions in latent spaces of generative models,...

Kirill Tamogashev, Esmeralda S. Whitammer · arXiv.org · 4 citations