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) …

We introduce and study the online pause and resume problem. In this problem, a player
attempts to find the k lowest (alternatively, highest) prices in a sequence of fixed length T …

Online optimization with memory costs has many real-world applications, where sequential
actions are made without knowing the future input. Nonetheless, the memory cost couples …

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 …

We propose a learning-augmented algorithm, ROBUSTML, for operation of dispatchable
generation that exploits the good performance of a machine-learned algorithm while …

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

A Complete Bibliography of Proceedings of the ACM on Measurement and Analysis of
Computing Systems (POMACS) Page 1 A Complete Bibliography of Proceedings of the …