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Christophe Vauthier

2 accepted papers

2025

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows

ICML 2025oral

Many applications in machine learning involve data represented as probability distributions. The emergence of such data requires radically novel techniques to design tractable gradient flows on probability distributions over this type of (infinite-dimensional) objects. For instance, being able to fl…

2025

Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysis

ICML 2025poster

In this paper, we investigate the properties of the Sliced Wasserstein Distance (SW) when employed as an objective functional. The SW metric has gained significant interest in the optimal transport and machine learning literature, due to its ability to capture intricate geometric properties of proba…

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