The field of digital fashion is rapidly evolving, yet what constitutes digital fashion, and how it
should be defined has not been firmly established. This study aims to conceptualize and …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

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

Multi-armed bandit problems are the predominant theoretical model of exploration-
exploitation tradeoffs in learning, and they have countless applications ranging from medical …

We consider a price-based network revenue management problem in which a retailer aims
to maximize revenue from multiple products with limited inventory over a finite selling …

We consider the problem faced by a firm that receives highly differentiated products in an
online fashion. The firm needs to price these products to sell them to its customer base …

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed
bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a …

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 …

We consider contextual bandits with linear constraints (CBwLC), a variant of contextual
bandits in which the algorithm consumes multiple resources subject to linear constraints on …

In online advertising markets, setting budget and return on investment (ROI) constraints are
two prevalent ways to help advertisers (ie buyers) utilize limited monetary resources …