2 citations · 5 across the 7 of their papers we have counts for
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Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows
Mingxuan Yi, Song Liu
Variational inference is a technique that approximates a target distribution by optimizing within the parameter space of variational families. On the other hand, Wasserstein gradie…
Minimizing -Divergences by Interpolating Velocity Fields
Song Liu, Jiahao Yu, Jack Simons +2
Many machine learning problems can be seen as approximating a \textit{target} distribution using a \textit{particle} distribution by minimizing their statistical discrepancy. Wasse…
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
Mingxuan Yi, Zhanxing Zhu, Song Liu
The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator le…
Sliced Wasserstein Variational Inference
Mingxuan Yi, Song Liu
Variational Inference approximates an unnormalized distribution via the minimization of Kullback-Leibler (KL) divergence. Although this divergence is efficient for computation and…
Posterior Ratio Estimation of Latent Variables
Song Liu, Yulong Zhang, Mingxuan Yi +1
Density Ratio Estimation has attracted attention from the machine learning community due to its ability to compare the underlying distributions of two datasets. However, in some ap…