no code implementations • 11 Apr 2024 • Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
We study an auction setting in which bidders bid for placement of their content within a summary generated by a large language model (LLM), e. g., an ad auction in which the display is a summary paragraph of multiple ads.
no code implementations • 16 Mar 2024 • Martino Banchio, Aranyak Mehta, Andres Perlroth
We are motivated by online advertising auctions when users interact with a platform over the course of a session.
no code implementations • 16 Feb 2023 • Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas
We characterize the optimal mechanism for this MDP as a Myerson's auction with a notion of modified virtual value, which relies on the value distribution of the advertiser, the current user state, and the future impact of showing the ad to the user.
no code implementations • 31 Jan 2023 • Yeganeh Alimohammadi, Aranyak Mehta, Andres Perlroth
Through the analysis of this model, we uncover a surprising result: in auto-bidding with two advertisers, FPA and SPA are auction equivalent.
no code implementations • 18 Jan 2023 • Aranyak Mehta, Andres Perlroth
We consider a multi-stage game where first the auctioneer declares the auction rules; then bidders select either the tCPA or mCPA bidding format and then, if the auctioneer lacks commitment, it can revisit the rules of the auction (e. g., may readjust reserve prices depending on the observed bids).
no code implementations • NeurIPS 2020 • Aranyak Mehta, Uri Nadav, Alexandros Psomas, Aviad Rubinstein
We consider the fundamental problem of selecting $k$ out of $n$ random variables in a way that the expected highest or second-highest value is maximized.
no code implementations • 16 Oct 2020 • Goran Zuzic, Di Wang, Aranyak Mehta, D. Sivakumar
In this paper, we focus on the AdWords problem, which is a classical online budgeted matching problem of both theoretical and practical significance.
no code implementations • 25 Sep 2019 • Goran Zuzic, Di Wang, Aranyak Mehta, D. Sivakumar
To answer this question, we draw insights from classic results in game theory, analysis of algorithms, and online learning to introduce a novel framework.
no code implementations • ICLR 2019 • Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar
This paper introduces a novel framework for learning algorithms to solve online combinatorial optimization problems.