6 code implementations • NeurIPS 2021 • Alexander Korotin, Lingxiao Li, Aude Genevay, Justin Solomon, Alexander Filippov, Evgeny Burnaev
Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance.
3 code implementations • NeurIPS 2021 • Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin Solomon, Evgeny Burnaev
Specifically, Fokker-Planck equations, which model the diffusion of probability measures, can be understood as gradient descent over entropy functionals in Wasserstein space.
no code implementations • 25 Feb 2021 • Gaspard Beugnot, Aude Genevay, Kristjan Greenewald, Justin Solomon
Optimal transport (OT) is a popular tool in machine learning to compare probability measures geometrically, but it comes with substantial computational burden.
1 code implementation • NeurIPS 2020 • Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin Solomon
Leveraging a new dual formulation for the regularized Wasserstein barycenter problem, we introduce a stochastic algorithm that constructs a continuous approximation of the barycenter.
no code implementations • 20 Oct 2019 • Aude Genevay, Gabriel Dulac-Arnold, Jean-Philippe Vert
Clustering is a fundamental unsupervised learning approach.
no code implementations • 18 May 2018 • Sebastian Claici, Aude Genevay, Justin Solomon
The proliferation of large data sets and Bayesian inference techniques motivates demand for better data sparsification.
no code implementations • 6 Jun 2017 • Aude Genevay, Gabriel Peyré, Marco Cuturi
This short article revisits some of the ideas introduced in arXiv:1701. 07875 and arXiv:1705. 07642 in a simple setup.
2 code implementations • 1 Jun 2017 • Aude Genevay, Gabriel Peyré, Marco Cuturi
The ability to compare two degenerate probability distributions (i. e. two probability distributions supported on two distinct low-dimensional manifolds living in a much higher-dimensional space) is a crucial problem arising in the estimation of generative models for high-dimensional observations such as those arising in computer vision or natural language.