In recent years, with the rapid development of current Internet and mobile communication
technologies, the infrastructure, devices and resources in networking systems are becoming …

We consider online allocation problems with concave revenue functions and resource
constraints, which are central problems in revenue management and online advertising. In …

Maximising a cumulative reward function that is Markov and stationary, ie, defined over state-
action pairs and independent of time, is sufficient to capture many kinds of goals in a Markov …

We present an adaptive online gradient descent algorithm to solve online convex
optimization problems with long-term constraints, which are constraints that need to be …

We consider the linear contextual bandit problem with resource consumption, in addition to
reward generation. In each round, the outcome of pulling an arm is a reward as well as a …

This paper studies the classic price-based network revenue management (NRM) problem
with demand learning. The retailer dynamically decides prices of n products over a finite …

Autobidding is becoming increasingly important in the domain of online advertising, and has
become a critical tool used by many advertisers for optimizing their ad campaigns. We …

We study the allocative challenges that governmental and nonprofit organizations face when
tasked with equitable and efficient rationing of a social good among agents whose needs …

Motivated by the success of using black-box predictive algorithms as subroutines for online
decision-making, we develop a new framework for designing online policies given access to …

Online allocation problems with resource constraints have a rich history in computer science
and operations research. In this paper, we introduce the regularized online allocation …