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Found 6 papers, 3 papers with code
Generating Synthetic Datasets by Interpolating along Generalized Geodesics
We compute these geodesics using a recent notion of distance between labeled datasets, and derive alternative interpolation schemes based on it: using either barycentric projections or optimal transport maps, the latter computed using recent neural OT methods.
Improved dimension dependence of a proximal algorithm for sampling
For instance, for strongly log-concave distributions, our method has complexity bound $\tilde\mathcal{O}(\kappa d^{1/2})$ without warm start, better than the minimax bound for MALA.
Variational Wasserstein gradient flow
Wasserstein gradient flow has emerged as a promising approach to solve optimization problems over the space of probability distributions.
On the complexity of the optimal transport problem with graph-structured cost
Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals.
Neural Monge Map estimation and its applications
Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another.
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Wasserstein Barycenter is a principled approach to represent the weighted mean of a given set of probability distributions, utilizing the geometry induced by optimal transport.
