Alexey Kroshnin — 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

Improved Stochastic Optimization of LogSumExp

Egor Gladin, Alexey Kroshnin, Jia-Jie Zhu, Pavel Dvurechenskii

ICML 2026poster

The LogSumExp function, dual to the Kullback-Leibler (KL) divergence, plays a central role in many important optimization problems, including entropy-regularized optimal transport (OT) and distributionally robust optimization (DRO). In practice, when the number of exponential terms inside the logari…

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2019

On the Complexity of Approximating Wasserstein Barycenters

Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Cesar Uribe

ICML 2019oral

We study the complexity of approximating the Wasserstein barycenter of $m$ discrete measures, or histograms of size $n$, by contrasting two alternative approaches that use entropic regularization. The first approach is based on the Iterative Bregman Projections (IBP) algorithm for which our novel an…

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2018

Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm

Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin

ICML 2018oral

We analyze two algorithms for approximating the general optimal transport (OT) distance between two discrete distributions of size $n$, up to accuracy $\varepsilon$. For the first algorithm, which is based on the celebrated Sinkhorn’s algorithm, we prove the complexity bound $\widetilde{O}\left(\fra…

Cited by 362 Source ↗PDF ↗Scholar ↗