Nikita Maksimovich Kornilov — accepted AI-conference papers

2026

Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting

Sergei Kholkin, Grigoriy Ksenofontov, David Li, Nikita Maksimovich Kornilov, Nikita Gushchin, Alexandra Suvorikova +3

ICLR 2026poster

The Iterative Markovian Fitting (IMF) procedure, which iteratively projects onto the space of Markov processes and the reciprocal class, successfully solves the Schrödinger Bridge (SB) problem. However, an efficient practical implementation requires a heuristic modification-alternating between fitti…

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2026

Universal Inverse Distillation for Matching Models with Real-Data Supervision (No GANs)

Nikita Maksimovich Kornilov, David Li, Tikhon Mavrin, Aleksei Leonov, Nikita Gushchin, Evgeny Burnaev +2

ICLR 2026oral

While achieving exceptional generative quality, modern diffusion, flow, and other matching models suffer from slow inference, as they require many steps of iterative generation. Recent distillation methods address this by training efficient one-step generators under the guidance of a pre-trained tea…

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2024

Optimal Flow Matching: Learning Straight Trajectories in Just One Step

Nikita Maksimovich Kornilov, Petr Mokrov, Alexander Gasnikov, Alexander Korotin

NeurIPS 2024poster

Over the several recent years, there has been a boom in development of Flow Matching (FM) methods for generative modeling. One intriguing property pursued by the community is the ability to learn flows with straight trajectories which realize the Optimal Transport (OT) displacements. Straightness is…

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