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.

翻译：我们探索了非线性MCMC技术的应用(首先在[1]年引入),用于解决巴伊西亚机器学习的问题;我们提供了全变式的趋同保证,利用新结果实现长期趋同和大型物品(“混乱的恢复”)趋同;我们将这种非线性MCMC技术用于取样问题,包括CIFAR10上的巴伊斯神经网络。