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Found 7 papers, 4 papers with code
Truthful Aggregation of LLMs with an Application to Online Advertising
Experimental results confirm that our mechanism not only converges efficiently to the optimally fine-tuned LLM but also significantly boosts advertiser value and platform revenue, all with minimal computational overhead.
Scalable Mechanism Design for Multi-Agent Path Finding
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.
Machine Learning-Powered Combinatorial Clock Auction
In this paper, we address this shortcoming by designing an ML-powered combinatorial clock auction that elicits information from the bidders only via demand queries (i. e., ``At prices $p$, what is your most preferred bundle of items?'').
Machine Learning-Powered Course Allocation
We study the course allocation problem, where universities assign course schedules to students.
Bayesian Optimization-based Combinatorial Assignment
In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism.
NOMU: Neural Optimization-based Model Uncertainty
To isolate the effect of model uncertainty, we focus on a noiseless setting with scarce training data.
FedMark: A Marketplace for Federated Data on the Web
The problem is that it is not clear how publishers of commercial data can monetize their data in this new setting.
Databases
