Search Results for author: Michael J. Curry

Found 7 papers, 2 papers with code

Learning Revenue-Maximizing Auctions With Differentiable Matching

no code implementations15 Jun 2021 Michael J. Curry, Uro Lyi, Tom Goldstein, John Dickerson

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

1 code implementation NeurIPS 2021 Neehar Peri, Michael J. Curry, Samuel Dooley, John P. Dickerson

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

no code implementations13 Oct 2020 Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson

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

1 code implementation7 Jul 2020 Ping-Yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein

While adversarial training can improve the empirical robustness of image classifiers, a direct extension to object detection is very expensive.

Object Detection

Certifying Strategyproof Auction Networks

no code implementations NeurIPS 2020 Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson

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.

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