Publications (A* and Q1) and pre-prints

Daniil Shlenskii, Alexander Varlamov, Nazar Buzun, Alexander Korotin (2026). Unlocking the Duality between Flow and Field Matching. In NeurIPS 2026 (Poster).

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Xavier Aramayo Carrasco, Maksim Nekrashevich, Petr Mokrov, Evgeny Burnaev, Alexander Korotin (2026). Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem. In NeurIPS 2026 (Poster).

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Arseny Ivanov, Sergei Kholkin, Vladislav Gromadskii, Grigoriy Ksenofontov, Ivan Oseledets, Alexander Korotin (2026). TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models. In NeurIPS 2026 (Poster).

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Dmitry Osin, Egor Surkov, Petr Mokrov, Igor Udovichenko, Ivan Karpukhin, Evgeny Burnaev, Egor Shvetsov, Alexander Korotin (2026). Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective. In NeurIPS 2026 (Spotlight).

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Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy, Dmitry V. Dylov, Alexander Korotin (2026). Midpoint Generative Models. In NeurIPS 2026 (Poster).

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Viacheslav Meshchaninov, Alexander Shabalin, Egor Chimbulatov, Nikita Gushchin, Ilya Koziev, Alexander Korotin, Dmitry Vetrov (2026). How to Train Your Latent Diffusion Language Model Jointly With the Latent Space. In NeurIPS 2026 (Poster).

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Daniil Shlenskii, S. I. Manukhov, Alexander Kolesov, Alexander Varlamov, Nazar Buzun, V. V. Palyulin, Alexander Korotin (2026). Capacitor Chain Field Matching for Electrostatic Generative Modeling. Unpublished.

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Mikhail Persiianov, Alexander Korotin (2026). Discrete Action Matching: Learning Stochastic Dynamics from Samples via State Graphs. A pre-print.

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David Li, Chanhyuk Lee, Jaehoon Yoo, Nikita Gushchin, Floor Eijkelboom, Eric Moulines, Maxim Panov, Alexander Korotin, Seunghoon Hong, Jinwoo Kim (2026). One-Step Generation via Riemannian Wasserstein Gradient Flows. A pre-print.

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Vladislav Gromadskii, David Li, Samson Gourevitch, Yazid Janati, Eric Moulines, Maxim Panov, Alexander Korotin (2026). IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models. A pre-print.

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Xavier Aramayo Carrasco, Petr Mokrov, Alexander Korotin (2026). Light Entropic Optimal Transport on Riemannian Manifolds. A pre-print.

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Kirill Sokolov, Alexander Korotin (2026). Exponential convergence rate for Iterative Markovian Fitting. In Uspekhi Matematicheskikh Nauk (Russian edition).

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Nikita Gushchin, Dmitry Baranchuk, Alexander Korotin (2026). Alpha Diffusion Language Models: Factorization Alone Is Not the Problem. A pre-print.

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Arseny Ivanov, Alexander Kolesov, Alexander Korotin, Ivan Oseledets, Mikhail Goncharov (2026). E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models. A pre-print.

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Milena Gazdieva, Kirill Sokolov, Jiawei Chen, Evgeny Burnaev, Alexander Korotin (2026). Simultaneous Neural Optimal Transport. A pre-print.

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Aleksei Leonov, Nikita Kornilov, Zhenhe Zhang, Evgeny Burnaev, Iaroslav Koshelev, Alexander Korotin (2026). Data Unlearning via Inverse Distillation. A pre-print.

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Sergei Kholkin, Evgeny Burnaev, Alexander Korotin (2026). Tilted Schrödinger Bridge Matching. A pre-print.

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Sergei Kholkin, Kirill Sokolov, Dmitry Baranchuk, Evgeny Burnaev, Alexander Korotin (2026). Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation. A pre-print.

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Alexander Zaytsev, Dmitry Baranchuk, Alexander Korotin, Aibek Alanov (2026). Rethinking Pixel Mean Flows via Interval Denoiser. A pre-print.

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Roman Dyachenko, Nikita Gushchin, Kirill Sokolov, Petr Mokrov, Evgeny Burnaev, Alexander Korotin (2026). Variational Entropic Optimal Transport. In ICML 2026.

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Daniil Shlenskii, Alexander Korotin (2026). Overclocking Electrostatic Generative Models. In ICML 2026.

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Daniil Selikhanovych, David Li, Alexey Leonov, Nikita Gushchin, Sergei Kushneriuk, Alexander Filippov, Evgeny Burnaev, Iaroslav Koshelev, Alexander Korotin (2026). One-Step Residual Shifting Diffusion for Image Super-Resolution via Distillation. In ICML 2026.

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Mikhail Persiianov, Arip Asadulaev, Nikita Andreev, Nikita Starodubcev, Dmitry Baranchuk, Anastasis Kratsios, Evgeny Burnaev, Alexander Korotin (2026). Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization. In ICML 2026.

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David Li, Nikita Gushchin, Dmitry Abulkhanov, Eric Moulines, Ivan Oseledets, Maxim Panov, Alexander Korotin (2026). IDLM: Inverse-distilled Diffusion Language Models. In ICML 2026.

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Nikita Kornilov, David Li, Tikhon Mavrin, Aleksei Leonov, Nikita Gushchin, Evgeny Burnaev, Iaroslav Koshelev, Alexander Korotin (2026). Universal Inverse Distillation for Matching Models with Real-Data Supervision (No GANs). In ICLR 2026 (Oral, Top 5% of accepted papers).

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Mikhail Persiianov, Jiawei Chen, Petr Mokrov, Alexander Tyurin, Evgeny Burnaev, Alexander Korotin (2026). Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme. In ICLR 2026.

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Manukhov Stepan, Alexander Kolesov, Vladimir Palyulin, Alexander Korotin (2026). Interaction Field Matching: Overcoming Limitations of Electrostatic Models. In ICLR 2026.

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Sergei Kholkin, Ivan Butakov, Evgeny Burnaev, Nikita Gushchin, Alexander Korotin (2026). InfoBridge: Mutual Information estimation via Bridge Matching. In ICLR 2026.

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Xavier Aramayo Carrasco, Grigory Ksenofontov, Aleksei Leonov, Iaroslav Koshelev, Alexander Korotin (2026). Entering the Era of Discrete Diffusion Models: A Benchmark for Schrödinger Bridges and Entropic Optimal Transport. In ICLR 2026.

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Sergei Kholkin, Grigory Ksenofontov, David Li, Nikita Kornilov, Nikita Gushchin, Alexandra Suvorikova, Alexey Kroshnin, Evgeny Burnaev, Alexander Korotin (2026). Diffusion and adversarial schrodinger bridges via iterative proportional markovian fitting. In ICLR 2026.

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Roman Tarasov, Petr Mokrov, Milena Gazdieva, Evgeny Burnaev, Alexander Korotin (2026). A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers. In ICLR 2026.

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Iryna Zabarianska, Sergei Kholkin, Grigoriy Ksenofontov, Ivan Butakov, Alexander Korotin (2026). Discrete Bridges for Mutual Information Estimation. A pre-print.

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Alexander Korotin, Gudmund Pammer (2025). On the inverse problem of flow matching in the one-dimensional and Gaussian cases. In RMS.

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Viacheslav Vasilev, Arseny Ivanov, Nikita Gushchin, Maria Kovaleva, Alexander Korotin (2025). Time-Correlated Video Bridge Matching. A pre-print.

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Nikita Kornilov, Alexander Korotin (2025). On the equivalence of optimal transport problem and action matching with optimal vector fields. In RMS.

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Milena Gazdieva, Petr Mokrov, Litu Rout, Alexander Korotin, Andrey Kravchenko, Alexander Filippov, Evgeny Burnaev (2025). An optimal transport perspective on unpaired image super-resolution. In JOTA.

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Nikita Gushchin, David Li, Daniil Selikhanovych, Evgeny Burnaev, Dmitry Baranchuk, Alexander Korotin (2025). Inverse Bridge Matching Distillation. In ICML 2025.

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Alexander Kolesov, Manukhov Stepan, Vladimir Palyulin, Alexander Korotin (2025). Field Matching: an Electrostatic Paradigm to Generate and Transfer Data. In ICML 2025.

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Grigory Ksenofontov, Alexander Korotin (2025). Categorical Schrodinger Bridge Matching. In ICML 2025.

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Igor Udovichenko, Olivier Croissant, Anita Toleutaeva, Evgeny Burnaev, Alexander Korotin (2025). Risk-Averse Reinforcement Learning with Itakura-Saito Loss. A pre-print.

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Milena Gazdieva, Jaemoo Choi, Alexander Kolesov, Jaewoong Choi, Petr Mokrov, Alexander Korotin (2025). Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport. In ICLR 2025.

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Milena Gazdieva, Arip Asadulaev, Evgeny Burnaev, Alexander Korotin (2024). Light Unbalanced Optimal Transport. In NeurIPS 2024.

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Alexander Kolesov, Petr Mokrov, Igor Udovichenko, Milena Gazdieva, Gudmund Pammer, Anastasis Kratsios, Evgeny Burnaev, Alexander Korotin (2024). Energy-Guided Continuous Entropic Barycenter Estimation for General Costs. In NeurIPS 2024 (Spotlight).

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Arip Asadulaev, Rostislav Korst, Alexander Korotin, Vage Egiazarian, Andrey Filchenkov, Evgeny Burnaev (2024). Rethinking Optimal Transport in Offline Reinforcement Learning. In NeurIPS 2024.

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Nikita Gushchin, Sergey Kholkin, Evgeny Burnaev, Alexander Korotin (2024). Light and Optimal Schrodinger Bridge Matching. In ICML 2024.

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Alexander Kolesov, Petr Mokrov, Igor Udovichenko, Milena Gazdieva, Gudmund Pammer, Evgeny Burnaev, Alexander Korotin (2024). Estimating Barycenters of Distributions with Neural Optimal Transport. In ICML 2024.

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Arip Asadulaev, Alexander Korotin, Vage Egiazarian, Petr Mokrov, Evgeny Burnaev (2024). Neural Optimal Transport with General Cost Functionals. In ICLR 2024.

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Alexander Korotin, Nikita Gushchin, Evgeny Burnaev (2024). Light Schrödinger Bridge. In ICLR 2024.

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Petr Mokrov, Alexander Korotin, Alexander Kolesov, Nikita Gushchin, Evgeny Burnaev (2024). Energy-guided Entropic Neural Optimal Transport. In ICLR 2024.

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Nikita Gushchin, Daniil Selikhanovych, Sergey Kholkin, Evgeny Burnaev, Alexander Korotin (2024). Adversarial Schrödinger Bridge Matching. In NeurIPS 2024.

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Nikita Kornilov, Petr Mokrov, Alexander Gasnikov, Alexander Korotin (2024). Optimal Flow Matching - Learning Straight Trajectories in Just One Step. In NeurIPS 2024.

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Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev (2023). Extremal Domain Translation with Neural Optimal Transport. In NeurIPS 2023.

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Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry Vetrov, Evgeny Burnaev (2023). Entropic Neural Optimal Transport via Diffusion Processes. In NeurIPS 2023 (Oral, Top 3%).

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Nikita Gushchin, Alexander Kolesov, Petr Mokrov, Polina Karpikova, Andrei Spiridonov, Evgeny Burnaev, Alexander Korotin (2023). Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark. In NeurIPS 2023.

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Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev (2023). Neural Optimal Transport. In ICLR 2023 (Spotlight, Top 25%).

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Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev (2023). Kernel Neural Optimal Transport. In ICLR 2023.

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Alexander Korotin, Vage Egiazarian, Lingxiao Li, Evgeny Burnaev (2022). Wasserstein Iterative Networks for Barycenter Estimation. In NeurIPS 2022.

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Alexander Korotin, Alexander Kolesov, Evgeny Burnaev (2022). Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?. In NeurIPS 2022.

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Simon Abramov, Alexander Korotin, Andrey Somov, Evgeny Burnaev, Anton Stepanov, Dmitry Nikolaev, Maria A Titova (2022). Analysis of video game players’ emotions and team performance: An esports tournament case study. In IEEE Journal of Biomedical and Health Informatics.

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Litu Rout, Alexander Korotin, Evgeny Burnaev (2022). Generative Modeling with Optimal Transport Maps. In ICLR 2022.

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Alexander Korotin, Vladimir Vyugin, Evgeny Burnaev (2021). Mixability of integral losses: A key to efficient online aggregation of functional and probabilistic forecasts. In Pattern Recognition.

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Alexander Korotin, Vladimir Vyugin, Evgeny Burnaev (2021). Adaptive Hedging under Delayed Feedback. In Neurocomputing.

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Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev (2021). Manifold Topology Divergence: a Framework for Comparing Data Manifolds. In NeurIPS 2021.

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Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin Solomon, Evgeny Burnaev (2021). Large-Scale Wasserstein Gradient Flows. In NeurIPS 2021.

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Alexander Korotin, Lingxiao Li, Aude Genevay, Justin Solomon, Alexander Filippov, Evgeny Burnaev (2021). Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark. In NeurIPS 2021.

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Alexander Korotin, Vage Egiazarian, Arip Asadulaev, Alexander Safin, Evgeny Burnaev (2021). Wasserstein-2 Generative Networks. In ICLR 2021.

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Alexander Korotin, Lingxiao Li, Justin Solomon, Evgeny Burnaev (2021). Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization. In ICLR 2021.

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Alexander Korotin, Vladimir Vyugin, Evgeny Burnaev (2020). Online algorithm for aggregating experts predictions with unbounded quadratic loss. In Russian Mathematical Surveys.

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