25 citations · 52 across the 7 of their papers we have counts for
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stat.ML2018
Self-Adversarially Learned Bayesian Sampling
Yang Zhao, Jianyi Zhang, Changyou Chen
Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models. While tremendous progresse…
stat.ML2018
Variance Reduction in Stochastic Particle-Optimization Sampling
Jianyi Zhang, Yang Zhao, Changyou Chen
Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational…
stat.ML2018
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory
Jianyi Zhang, Ruiyi Zhang, Lawrence Carin +1
Particle-optimization-based sampling (POS) is a recently developed effective sampling technique that interactively updates a set of particles. A representative algorithm is the Ste…