196 citations · 253 across the 18 of their papers we have counts for
19 papers
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
Zhe Feng, Christopher Liaw, Zixin Zhou
In this work, we investigate the online learning problem of revenue maximization in ad auctions, where the seller needs to learn the click-through rates (CTRs) of each ad candidate…
The Power of Two-sided Recruitment in Two-sided Markets
Yang Cai, Christopher Liaw, Aranyak Mehta +1
We consider the problem of maximizing the gains from trade (GFT) in two-sided markets. The seminal impossibility result by Myerson and Satterthwaite shows that even for bilateral t…
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
Jamil Arbas, Hassan Ashtiani, Christopher Liaw
We study the problem of privately estimating the parameters of -dimensional Gaussian Mixture Models (GMMs) with components. For this, we develop a technique to reduce the pr…
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization
Yang Cai, Zhe Feng, Christopher Liaw +2
We propose a new Markov Decision Process (MDP) model for ad auctions to capture the user response to the quality of ads, with the objective of maximizing the long-term discounted r…
Efficiency of non-truthful auctions under auto-bidding
Christopher Liaw, Aranyak Mehta, Andres Perlroth
Auto-bidding is now widely adopted as an interface between advertisers and internet advertising as it allows advertisers to specify high-level goals, such as maximizing value subje…
Continuous Prediction with Experts' Advice
Victor Sanches Portella, Christopher Liaw, Nicholas J. A. Harvey
Prediction with experts' advice is one of the most fundamental problems in online learning and captures many of its technical challenges. A recent line of work has looked at online…