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Found 8 papers, 3 papers with code
Scalable Mechanism Design for Multi-Agent Path Finding
In this work, we introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms.
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.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
