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Found 9 papers, 2 papers with code
Truthful Aggregation of LLMs with an Application to Online Advertising
To address this, we introduce MOSAIC, an auction mechanism that ensures that truthful reporting is a dominant strategy for advertisers and that aligns the utility of each advertiser with their contribution to social welfare.
Optimal Automated Market Makers: Differentiable Economics and Strong Duality
The role of a market maker is to simultaneously offer to buy and sell quantities of goods, often a financial asset such as a share, at specified prices.
Learning Revenue-Maximizing Auctions With Differentiable Matching
We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations.
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
In addition, we introduce a new metric to evaluate an auction allocations' adherence to such socially desirable constraints and demonstrate that our proposed method is competitive with current state-of-the-art neural-network based auction designs.
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
Detection as Regression: Certified Object Detection by Median Smoothing
While adversarial training can improve the empirical robustness of image classifiers, a direct extension to object detection is very expensive.
Certifying Strategyproof Auction Networks
We focus on the RegretNet architecture, which can represent auctions with arbitrary numbers of items and participants; it is trained to be empirically strategyproof, but the property is never exactly verified leaving potential loopholes for market participants to exploit.
Headless Horseman: Adversarial Attacks on Transfer Learning Models
We first demonstrate successful transfer attacks against a victim network using \textit{only} its feature extractor.
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests.
