The problem of online matching with stochastic rewards is a generalization of the online bipartite matching problem where each edge has a probability of success. When a match is made it succeeds wi…
Markov decision processes (MDPs) are used to model stochastic systems in many applications. Several efficient algorithms to compute optimal policies have been studied in the literature, including v…
We consider a robust approach to address uncertainty in model parameters in Markov decision processes (MDPs), which are widely used to model dynamic optimization in many applications. Most prior wo…
One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions such as emails, display ads, and search ads to eventual conve…
We consider an online assortment optimization problem where we have n substitutable products with fixed reusable capacities c1,…,cn. In each period t, a user with some preferences (potentially ad…
Assortment optimization is an important problem that arises in many practical applications such as retailing and online advertising. In this problem, the goal is to select a subset of items that ma…
In this paper, we study the performance of affine policies for two-stage adjustable robust optimization problem with fixed recourse and uncertain right hand side belonging to a budgeted uncertainty…
Assortment optimization is an important problem arising in many applications, including retailing and online advertising. The goal in such problems is to determine a revenue-/profit-maximizing subs…