Search Results for author: John Dickerson

Found 14 papers, 3 papers with code

Achieving Downstream Fairness with Geometric Repair

no code implementations14 Mar 2022 Kweku Kwegyir-Aggrey, Jessica Dai, John Dickerson, Keegan Hines

Consider a scenario where some upstream model developer must train a fair model, but is unaware of the fairness requirements of a downstream model user or stakeholder.

Fairness

Differentiable Economics for Randomized Affine Maximizer Auctions

no code implementations6 Feb 2022 Michael Curry, Tuomas Sandholm, John Dickerson

We present an architecture that supports multiple bidders and is perfectly strategyproof, but cannot necessarily represent the optimal mechanism.

Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling

1 code implementation7 Oct 2021 Naveen Raman, Sanket Shah, John Dickerson

Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers.

Fairness

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.

Counterfactual Explanations for Machine Learning: Challenges Revisited

no code implementations14 Jun 2021 Sahil Verma, John Dickerson, Keegan Hines

Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models.

LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition

no code implementations ICLR 2021 Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John Dickerson, Gavin Taylor, Tom Goldstein

Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike.

Face Detection Face Recognition

Counterfactual Explanations for Machine Learning: A Review

no code implementations20 Oct 2020 Sahil Verma, John Dickerson, Keegan Hines

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders.

Counterfactual Explanation

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.

Robust Active Preference Elicitation

no code implementations4 Mar 2020 Phebe Vayanos, Yingxiao Ye, Duncan McElfresh, John Dickerson, Eric Rice

For the offline case, where active preference elicitation takes the form of a two and half stage robust optimization problem with decision-dependent information discovery, we provide an equivalent reformulation in the form of a mixed-binary linear program which we solve via column-and-constraint generation.

Recommendation Systems

Forming Diverse Teams from Sequentially Arriving People

no code implementations25 Feb 2020 Faez Ahmed, John Dickerson, Mark Fuge

Our method has applications in collaborative work ranging from team formation, the assignment of workers to teams in crowdsourcing, and reviewer allocation to journal papers arriving sequentially.

Universal Adversarial Training

no code implementations27 Nov 2018 Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S. Davis, Tom Goldstein

Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels.

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