The optimal policy in various real-world strategic decision-making problems depends both on the environmental configuration and exogenous events.

The development of score-based diffusion models is deeply rooted in the theory of stochastic processes.

We further propose a federated natural actor critic (NAC) method for multi-task RL with function approximation and stochastic policy evaluation, and establish its finite-time sample complexity taking the errors of function approximation into account.

Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables.

Such embeddings induce the so-called maximum mean discrepancy (MMD; [Smola et al., 2007, Gretton et al., 2012]), which quantifies the discrepancy Many estimators for HSIC exist. The classical ones rely on U-statistics or V -statistics [Gretton et al., 2005, Quadrianto et al., 2009, Pfister et al., 2018] and are known to converge at a rate of Lower bounds for the related MMD are known [Tolstikhin et al., 2016], but the existing analysis considers radial kernels and relies on independent Gaussian distributions.

The sampling method alternates between adding substantial noise in additional forward steps and strictly following a backward ODE.