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cs.LG2022★ 1 cited
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization
Yifei Wang, Peng Chen, Mert Pilanci +1
The computation of Wasserstein gradient direction is essential for posterior sampling problems and scientific computing. The approximation of the Wasserstein gradient with finite s…
cs.LG2021
Projected Wasserstein gradient descent for high-dimensional Bayesian inference
Yifei Wang, Peng Chen, Wuchen Li
We propose a projected Wasserstein gradient descent method (pWGD) for high-dimensional Bayesian inference problems. The underlying density function of a particle system of WGD is a…
cs.LG2020
Projected Stein Variational Gradient Descent
Peng Chen, Omar Ghattas
The curse of dimensionality is a longstanding challenge in Bayesian inference in high dimensions. In this work, we propose a projected Stein variational gradient descent (pSVGD) me…