We study online learning problems in which a decision maker has to take a sequence of
decisions subject to $ m $ long-term constraints. The goal of the decision maker is to …

Safe reinforcement learning is extremely challenging--not only must the agent explore an
unknown environment, it must do so while ensuring no safety constraint violations. We …

Distributed bandit online convex optimization with time-varying coupled inequality
constraints is considered, motivated by a repeated game between a group of learners and …

This paper considers online convex optimization with hard constraints and analyzes
achievable regret and cumulative hard constraint violation (violation for short). The problem …

Achieving low-latency is paramount for live streaming scenarios, that are now-days
becoming increasingly popular. In this paper, we propose a novel algorithm for bitrate …

We consider online convex optimization with stochastic constraints where the objective
functions are arbitrarily time-varying and the constraint functions are independent and …

We study online learning problems in which a decision maker has to make a sequence of
costly decisions, with the goal of maximizing their expected reward while adhering to budget …

In this article, we consider distributed online convex optimization problems, where the
distributed system consists of various computing units connected through a time-varying …

Caching refers to the act of replicating information at a faster (or closer) medium with the
purpose of improving performance. This deceptively simple idea has given rise to some of …

Today's online advertisers procure digital ad impressions through interacting with
autobidding platforms: advertisers convey high level procurement goals via setting levers …