Automated auction design aims to find empirically high-revenue mechanisms through
machine learning. Existing works on multi item auction scenarios can be roughly divided into …

Inspired by applications in pricing and contract design, we study the maximization of one-
sided Lipschitz functions, which only provide the (weaker) guarantee that they do not grow …

Motivated by pricing in ad exchange markets, we consider the problem of robust learning of
reserve prices against strategic buyers in repeated contextual second-price auctions …

Contextual dynamic pricing aims to set personalized prices based on sequential interactions
with customers. At each time period, a customer who is interested in purchasing a product …

In this article, we study the contextual dynamic pricing problem where the market value of a
product is linear in its observed features plus some market noise. Products are sold one at a …

In contextual dynamic pricing, a seller sequentially prices goods based on contextual
information. Buyers will purchase products only if the prices are below their valuations. The …

Dynamic pricing is a fast-moving research area in machine learning and operations
management. A lot of work has been done for this problem with known noise. In this paper …

The society's insatiable appetites for personal data are driving the emergence of data
markets, allowing data consumers to launch customized queries over the datasets collected …