The topic of dynamic pricing and learning has received a considerable amount of attention
in recent years, from different scientific communities. We survey these literature streams: we …

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 …

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

The increasingly tight coupling between humans and system operations in domains ranging
from intelligent infrastructure to e-commerce has led to a challenging new class of problems …

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 …

What price should be offered to a worker for a task in an online labor market? How can one
enable workers to express the amount they desire to receive for the task completion …

We consider a dynamic assortment selection problem where in every round the retailer
offers a subset (assortment) of N substitutable products to a consumer, who selects one of …

In this paper, we consider a very general model for exploration-exploitation tradeoff which
allows arbitrary concave rewards and convex constraints on the decisions across time, in …

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 introduce the online stochastic Convex Programming (CP) problem, a very general
version of stochastic online problems which allows arbitrary concave objectives and convex …