3 citations · 10 across the 10 of their papers we have counts for
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Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
Ashwinkumar Badanidiyuru
Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms. We introduce a novel framewor…
Selling Joint Ads: A Regret Minimization Perspective
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Paul Dütting +1
Motivated by online retail, we consider the problem of selling one item (e.g., an ad slot) to two non-excludable buyers (say, a merchant and a brand). This problem captures, for ex…
Auto-bidding and Auctions in Online Advertising: A Survey
Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro +23
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opp…
Auctioning with Strategically Reticent Bidders
Jibang Wu, Ashwinkumar Badanidiyuru, Haifeng Xu
We propose and study a novel mechanism design setup where each bidder holds two kinds of private information: (1) type variable, which can be misreported; (2) information variable,…
Response Prediction for Low-Regret Agents
Saeed Alaei, Ashwinkumar Badanidiyuru, Mohammad Mahdian +1
Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on…
Targeting and Signaling in Ad Auctions
Ashwinkumar Badanidiyuru, Kshipra Bhawalkar, Haifeng Xu
Modern ad auctions allow advertisers to target more specific segments of the user population. Unfortunately, this is not always in the best interest of the ad platform. In this pap…