In this paper we consider clustering problems in which each point is endowed with a color.
The goal is to cluster the points to minimize the classical clustering cost but with the …

In classic auction theory, reserve prices are known to be effective for improving revenue for
the auctioneer against quasi-linear utility maximizing bidders. The introduction of reserve …

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 …

Online auctions are one of the most fundamental facets of the modern economy and power
an industry generating hundreds of billions of dollars a year in revenue. Auction theory has …

In many online platforms, customers' decisions are substantially influenced by product
rankings as most customers only examine a few top-ranked products. Concurrently, such …

We consider a dynamic pricing problem for repeated contextual second-price auctions with
multiple strategic buyers who aim to maximize their long-term time discounted utility. The …

Data fuels machine learning (ML)-rich and high-quality training data is essential to the
success of ML. However, to transform ML from the race among a few large corporations to …

We consider the problem of the optimization of bidding strategies in prior-dependent
revenue-maximizing auctions, when the seller fixes the reserve prices based on the bid …

We consider the problem of learning optimal reserve price in repeated auctions against non-
myopic bidders, who may bid strategically in order to gain in future rounds even if the single …

We study the problem of learning Bayesian-optimal revenue-maximizing auctions. The
classical approach to maximizing revenue requires a known prior distribution on the …