75 citations · 374 across the 37 of their papers we have counts for
10 papers · 1 filter
Scalable Thompson Sampling via Optimal Transport
Ruiyi Zhang, Zheng Wen, Changyou Chen +1
Thompson sampling (TS) is a class of algorithms for sequential decision-making, which requires maintaining a posterior distribution over a model. However, calculating exact posteri…
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…
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…
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…
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling
Changyou Chen, Ruiyi Zhang, Wenlin Wang +2
There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-dat…
On Connecting Stochastic Gradient MCMC and Differential Privacy
Bai Li, Changyou Chen, Hao Liu +1
Significant success has been realized recently on applying machine learning to real-world applications. There have also been corresponding concerns on the privacy of training data,…