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Found 5 papers, 1 papers with code
Incorporating Unlabeled Data into Distributionally Robust Learning
We examine the performance of this new formulation on 14 real datasets and find that it often yields effective classifiers with nontrivial performance guarantees in situations where conventional DRL produces neither.
Learning Embeddings into Entropic Wasserstein Spaces
Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions.
Learning Entropic Wasserstein Embeddings
Despite their prevalence, Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions.
Approximate inference with Wasserstein gradient flows
We present a novel approximate inference method for diffusion processes, based on the Wasserstein gradient flow formulation of the diffusion.
Learning with a Wasserstein Loss
In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance.
