Multiple types of inference are available for probabilistic graphical models, eg, marginal,
maximum-a-posteriori, and even marginal maximum-a-posteriori. Which one do researchers …

This paper considers the problem of finding near-optimal Markovian randomized (MR)
policies for finite-state-action, infinite-horizon, constrained risk-sensitive Markov decision …

In this paper, we study the problem of dynamic channel allocation for URLLC traffic in a multi-
user multichannel wireless network where urgent packets have to be successfully received …

We consider the problem of optimally designing a system for repeated use under
uncertainty. We develop a modeling framework that integrates the design and operational …

We study the problem of learning optimal policies in finite-horizon Markov Decision
Processes (MDPs) using low-rank reinforcement learning (RL) methods. In finite-horizon …

The rapid development of network systems, driven by innovations like 5G communications,
Industrial 4.0, and Artificial Intelligence (AI)-assisted services, has led to a more complex …

There are no computationally feasible algorithms that provide solutions to the finite horizon
Risk-sensitive Con-strained Markov Decision Process (Risk-CMDP) problem, even for …

This letter considers the problem of optimizing a finite-horizon constrained Markov decision
process (CMDP) where the objective and constraints are sums of additive and multiplicative …

In this paper, we study the problem of dynamic channel allocation for URLLC traffic in a multi-
user multi-channel wireless network where urgent packets have to be successfully …

In this paper we study the problem of assessing the effectiveness of a proactive defense-by-
detection policy with a network-based moving target defense. We model the network system …