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Found 9 papers, 4 papers with code
LLM-Powered Preference Elicitation in Combinatorial Assignment
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment.
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.
Neural Auctions Compromise Bidder Information
It has been shown that neural networks can be used to approximate optimal mechanisms while satisfying the constraints that an auction be strategyproof and individually rational.
Certified Neural Network Watermarks with Randomized Smoothing
Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.
Differentiable Economics for Randomized Affine Maximizer Auctions
We present an architecture that supports multiple bidders and is perfectly strategyproof, but cannot necessarily represent the optimal mechanism.
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
We validate the learned solutions are $\epsilon$-meta-equilibria through best-response analyses, show that they align with economic intuitions, and show our approach can learn a spectrum of qualitatively distinct $\epsilon$-meta-equilibria in open RBC models.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Certified Watermarks for Neural Networks
Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.
Detection as Regression: Certified Object Detection with Median Smoothing
Despite the vulnerability of object detectors to adversarial attacks, very few defenses are known to date.
Improving Policy-Constrained Kidney Exchange via Pre-Screening
In barter exchanges, participants swap goods with one another without exchanging money; exchanges are often facilitated by a central clearinghouse, with the goal of maximizing the aggregate quality (or number) of swaps.
