1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
Bayesian Learning for Dynamic Inference
Aolin Xu, Peng Guan
The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential…
math.OC2014
Online Markov decision processes with Kullback-Leibler control cost
Peng Guan, Maxim Raginsky, Rebecca Willett
This paper considers an online (real-time) control problem that involves an agent performing a discrete-time random walk over a finite state space. The agent's action at each time…
cs.LG2013★ 1 cited
Relax but stay in control: from value to algorithms for online Markov decision processes
Peng Guan, Maxim Raginsky, Rebecca Willett
Online learning algorithms are designed to perform in non-stationary environments, but generally there is no notion of a dynamic state to model constraints on current and future ac…