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Mingxuan Yi

2 accepted papers

2024

Minimizing $f$-Divergences by Interpolating Velocity Fields

ICML 2024poster

Many machine learning problems can be seen as approximating a *target* distribution using a *particle* distribution by minimizing their statistical discrepancy. Wasserstein Gradient Flow can move particles along a path that minimizes the $f$-divergence between the target and particle distributions.…

2023

MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows

ICML 2023poster

The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator learns to minimize this divergence. We argue that despite the fact that many variants of GANs were developed following thi…

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