2 citations · 2 across the 5 of their papers we have counts for
6 papers
LLM-Powered Preference Elicitation in Combinatorial Assignment
Ermis Soumalias, Yanchen Jiang, Kehang Zhu +3
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment. While traditional PE methods rely…
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
Ermis Soumalias, Jakob Heiss, Jakob Weissteiner +1
We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address th…
Truthful Aggregation of LLMs with an Application to Online Advertising
Ermis Soumalias, Michael J. Curry, Sven Seuken
The next frontier of online advertising is revenue generation from LLM-generated content. We consider a setting where advertisers aim to influence the responses of an LLM to align…
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings
Michael Curry, Vinzenz Thoma, Darshan Chakrabarti +5
Dynamic mechanism design is a challenging extension to ordinary mechanism design in which the mechanism designer must make a sequence of decisions over time in the face of possibly…
Scalable Mechanism Design for Multi-Agent Path Finding
Paul Friedrich, Yulun Zhang, Michael Curry +5
Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This p…
Learning Best Response Policies in Dynamic Auctions via Deep Reinforcement Learning
Vinzenz Thoma, Michael Curry, Niao He +1
Many real-world auctions are dynamic processes, in which bidders interact and report information over multiple rounds with the auctioneer. The sequential decision making aspect pai…