18 citations · 98 across the 27 of their papers we have counts for
34 papers
Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
Dmitry Osin, Egor Surkov, Petr Mokrov +5
Event sequences are chains of random events, each described by heterogeneous (numerical and categorical) features. The problem of event sequence forecasting occurs in finance, heal…
Light Entropic Optimal Transport on Riemannian Manifolds
Xavier Aramayo-Carrasco, Petr Mokrov, Alexander Korotin
Entropic Optimal Transport (EOT) has become a practical framework for learning stochastic couplings between complex distributions, with applications in generative modeling and doma…
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…
Rethinking Pixel Mean Flows via Interval Denoiser
Alexander Zaytsev, Dmitry Baranchuk, Alexander Korotin +1
Modern diffusion and flow-based models are increasingly moving toward few-step, latent-free generation to bypass the computational overhead of multi-step sampling and the reconstru…
Online Algorithm for Aggregating Experts' Predictions with Unbounded Quadratic Loss
Alexander Korotin, Vladimir V'yugin, Evgeny Burnaev
We consider the problem of online aggregation of expert predictions with the quadratic loss function. We propose an algorithm for aggregating expert predictions which does not requ…
Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark
Nikita Gushchin, Alexander Kolesov, Petr Mokrov +4
Over the last several years, there has been significant progress in developing neural solvers for the Schrödinger Bridge (SB) problem and applying them to generative modelling. Thi…
Energy-guided Entropic Neural Optimal Transport
Petr Mokrov, Alexander Korotin, Alexander Kolesov +2
Energy-based models (EBMs) are known in the Machine Learning community for decades. Since the seminal works devoted to EBMs dating back to the noughties, there have been a lot of e…
Light Unbalanced Optimal Transport
Milena Gazdieva, Arip Asadulaev, Alexander Korotin +1
While the continuous Entropic Optimal Transport (EOT) field has been actively developing in recent years, it became evident that the classic EOT problem is prone to different issue…
Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem
Xavier Aramayo Carrasco, Maksim Nekrashevich, Petr Mokrov +2
Recently, the Gromov-Wasserstein Optimal Transport (GWOT) problem has attracted the special attention of the ML community. In this problem, given two distributions supported on two…
Extremal Domain Translation with Neural Optimal Transport
Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych +1
In many unpaired image domain translation problems, e.g., style transfer or super-resolution, it is important to keep the translated image similar to its respective input image. We…
Entropic Neural Optimal Transport via Diffusion Processes
Nikita Gushchin, Alexander Kolesov, Alexander Korotin +2
We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions which are access…
Connecting adversarial attacks and optimal transport for domain adaptation
Arip Asadulaev, Vitaly Shutov, Alexander Korotin +2
We present a novel algorithm for domain adaptation using optimal transport. In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the targ…
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
Alexander Korotin, Alexander Kolesov, Evgeny Burnaev
Wasserstein Generative Adversarial Networks (WGANs) are the popular generative models built on the theory of Optimal Transport (OT) and the Kantorovich duality. Despite the success…
Neural Optimal Transport with General Cost Functionals
Arip Asadulaev, Alexander Korotin, Vage Egiazarian +2
We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals. In contrast to common Euclidean costs, i.e., or $…
Kernel Neural Optimal Transport
Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak qua…
An Optimal Transport Perspective on Unpaired Image Super-Resolution
Milena Gazdieva, Petr Mokrov, Litu Rout +4
Real-world image super-resolution (SR) tasks often do not have paired datasets, which limits the application of supervised techniques. As a result, the tasks are usually approached…
Wasserstein Iterative Networks for Barycenter Estimation
Alexander Korotin, Vage Egiazarian, Lingxiao Li +1
Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way. In this paper, we present an al…
Neural Optimal Transport
Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev
We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prov…
Generative Modeling with Optimal Transport Maps
Litu Rout, Alexander Korotin, Evgeny Burnaev
With the discovery of Wasserstein GANs, Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. In these tasks, OT cost is typically used as th…
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov +4
We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, giv…
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Alexander Korotin, Lingxiao Li, Aude Genevay +3
Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance. In this paper, we add…
Large-Scale Wasserstein Gradient Flows
Petr Mokrov, Alexander Korotin, Lingxiao Li +3
Wasserstein gradient flows provide a powerful means of understanding and solving many diffusion equations. Specifically, Fokker-Planck equations, which model the diffusion of proba…
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Alexander Korotin, Lingxiao Li, Justin Solomon +1
Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to com…
Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
Serguei Barannikov, Daria Voronkova, Alexander Mironenko +4
Neural network training is commonly based on SGD. However, the understanding of SGD's ability to converge to good local minima, given the non-convex nature of loss functions and th…