355 citations · 388 across the 4 of their papers we have counts for
4 papers
Incorporating Unlabeled Data into Distributionally Robust Learning
Charlie Frogner, Sebastian Claici, Edward Chien +1
We study a robust alternative to empirical risk minimization called distributionally robust learning (DRL), in which one learns to perform against an adversary who can choose the d…
Learning Embeddings into Entropic Wasserstein Spaces
Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon
Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we cons…
Approximate inference with Wasserstein gradient flows
Charlie Frogner, Tomaso Poggio
We present a novel approximate inference method for diffusion processes, based on the Wasserstein gradient flow formulation of the diffusion. In this formulation, the time-dependen…
Learning with a Wasserstein Loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi +2
Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we deve…