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 …

One of the central problems in auction design is developing an incentive-compatible
mechanism that maximizes the auctioneer's expected revenue. While theoretical …

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 …

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 …

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 …

The agency problem emerges in today's large scale machine learning tasks, where the
learners are unable to direct content creation or enforce data collection. In this work, we …

In feature-based dynamic pricing, a seller sets appropriate prices for a sequence of products
(described by feature vectors) on the fly by learning from the binary outcomes of previous …

In today's data-rich environment, context-based dynamic pricing has gained much attention.
To model the demand as a function of price and context, the existing literature either adopts …