This paper studies the problem of Anytime-Competitive Markov Decision Process (A-CMDP).
Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the …

We study a general form of Smoothed Online Convex Optimization, aka SOCO, including
multi-step switching costs and feedback delay. We propose a novel machine learning (ML) …

This paper studies learning-augmented decentralized online convex optimization in a
networked multi-agent system, a challenging setting that has remained under-explored. We …

This paper studies online resource allocation with replenishable budgets, where budgets
can be replenished on top of the initial budget and an agent sequentially chooses online …

In cloud services, autoscaling is one of the most important features, which enables system
intelligence to adaptively assign computing resources for users according to real-time …

Many mission-critical systems need to solve online decision-making problems such as
workload scheduling in datacenters, power allocation in edge computing, battery …