Nonlinear MCMC for Bayesian Machine Learning

被引：0
|
作者：
Vuckovic, James
机构：
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022) | 2022年
关键词：
CHAIN MONTE-CARLO; MARKOV-PROCESSES; LANGEVIN; SIMULATION; ERGODICITY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We explore the application of a nonlinear MCMC technique first introduced in [1] to problems in Bayesian machine learning. We provide a convergence guarantee in total variation that uses novel results for long-time convergence and large-particle ("propagation of chaos") convergence. We apply this nonlinear MCMC technique to sampling problems including a Bayesian neural network on CIFAR10.
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页数：14
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