Recent advances in optimal transport for machine learning
EF Montesuma, FMN Mboula… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …
Learning for comparing and manipulating probability distributions. This is rooted in its rich …
Unbalanced minibatch optimal transport; applications to domain adaptation
Optimal transport distances have found many applications in machine learning for their
capacity to compare non-parametric probability distributions. Yet their algorithmic complexity …
capacity to compare non-parametric probability distributions. Yet their algorithmic complexity …
Projection‐based techniques for high‐dimensional optimal transport problems
Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …
measures, such that the transformation has the minimum transportation cost. Such a …
Distributional sliced-Wasserstein and applications to generative modeling
Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-
SW), have been used widely in the recent years due to their fast computation and scalability …
SW), have been used widely in the recent years due to their fast computation and scalability …
Statistical inference with regularized optimal transport
Optimal transport (OT) is a versatile framework for comparing probability measures, with
many applications to statistics, machine learning and applied mathematics. However, OT …
many applications to statistics, machine learning and applied mathematics. However, OT …
On projection robust optimal transport: Sample complexity and model misspecification
Optimal transport (OT) distances are increasingly used as loss functions for statistical
inference, notably in the learning of generative models or supervised learning. Yet, the …
inference, notably in the learning of generative models or supervised learning. Yet, the …
Computational guarantees for doubly entropic wasserstein barycenters
T Vaskevicius, L Chizat - Advances in Neural Information …, 2024 - proceedings.neurips.cc
We study the computation of doubly regularized Wasserstein barycenters, a recently
introduced family of entropic barycenters governed by inner and outer regularization …
introduced family of entropic barycenters governed by inner and outer regularization …
Asymptotic guarantees for learning generative models with the sliced-Wasserstein distance
Minimum expected distance estimation (MEDE) algorithms have been widely used for
probabilistic models with intractable likelihood functions and they have become increasingly …
probabilistic models with intractable likelihood functions and they have become increasingly …
Minibatch optimal transport distances; analysis and applications
Optimal transport distances have become a classic tool to compare probability distributions
and have found many applications in machine learning. Yet, despite recent algorithmic …
and have found many applications in machine learning. Yet, despite recent algorithmic …
Jointly imputing multi-view data with optimal transport
The multi-view data with incomplete information hinder the effective data analysis. Existing
multi-view imputation methods that learn the mapping between complete view and …
multi-view imputation methods that learn the mapping between complete view and …