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Found 9 papers, 0 papers with code
Auctions with LLM Summaries
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.
Auctions with Dynamic Scoring
We are motivated by online advertising auctions when users interact with a platform over the course of a session.
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization
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 revenue.
Incentive Compatibility in the Auto-bidding World
Through the analysis of this model, we uncover a surprising result: in auto-bidding with two advertisers, FPA and SPA are auction equivalent.
Auctions without commitment in the auto-bidding world
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).
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics
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.
Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training
In this paper, we focus on the AdWords problem, which is a classical online budgeted matching problem of both theoretical and practical significance.
YaoGAN: Learning Worst-case Competitive Algorithms from Self-generated Inputs
To answer this question, we draw insights from classic results in game theory, analysis of algorithms, and online learning to introduce a novel framework.
A new dog learns old tricks: RL finds classic optimization algorithms
This paper introduces a novel framework for learning algorithms to solve online combinatorial optimization problems.
