4 papers
Accelerated Regularized Wasserstein Proximal Sampling Algorithms
Hong Ye Tan, Stanley Osher, Wuchen Li
We consider sampling from a Gibbs distribution by evolving a finite number of particles using a particular score estimator rather than Brownian motion. To accelerate the particles,…
Sparse Transformer Architectures via Regularized Wasserstein Proximal Operator with Prior
Fuqun Han, Stanley Osher, Wuchen Li
In this work, we propose a sparse transformer architecture that incorporates prior information about the underlying data distribution directly into the transformer structure of the…
Variational conditional normalizing flows for computing second-order mean field control problems
Jiaxi Zhao, Mo Zhou, Xinzhe Zuo +1
Mean field control (MFC) problems have vast applications in artificial intelligence, engineering, and economics, while solving MFC problems accurately and efficiently in high-dimen…
Splitting Regularized Wasserstein Proximal Algorithms for Nonsmooth Sampling Problems
Fuqun Han, Stanley Osher, Wuchen Li
Sampling from nonsmooth target probability distributions is essential in various applications, including the Bayesian Lasso. We propose a splitting-based sampling algorithm for the…