Search Results for author: John Dickerson

Found 18 papers, 4 papers with code

Fair Polylog-Approximate Low-Cost Hierarchical Clustering

no code implementations21 Nov 2023 Marina Knittel, Max Springer, John Dickerson, Mohammadtaghi Hajiaghayi

Research in fair machine learning, and particularly clustering, has been crucial in recent years given the many ethical controversies that modern intelligent systems have posed.

Clustering Fairness

Artificial Intelligence/Operations Research Workshop 2 Report Out

no code implementations10 Apr 2023 John Dickerson, Bistra Dilkina, Yu Ding, Swati Gupta, Pascal Van Hentenryck, Sven Koenig, Ramayya Krishnan, Radhika Kulkarni, Catherine Gill, Haley Griffin, Maddy Hunter, Ann Schwartz

This workshop Report Out focuses on the foundational elements of trustworthy AI and OR technology, and how to ensure all AI and OR systems implement these elements in their system designs.

Fairness

Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

no code implementations23 Mar 2023 Avi Schwarzschild, Max Cembalest, Karthik Rao, Keegan Hines, John Dickerson

We observe on three datasets that we can train a model with this loss term to improve explanation consensus on unseen data, and see improved consensus between explainers other than those used in the loss term.

Neural Auctions Compromise Bidder Information

1 code implementation28 Feb 2023 Alex Stein, Avi Schwarzschild, Michael Curry, Tom Goldstein, John Dickerson

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.

Targets in Reinforcement Learning to solve Stackelberg Security Games

no code implementations30 Nov 2022 Saptarashmi Bandyopadhyay, Chenqi Zhu, Philip Daniel, Joshua Morrison, Ethan Shay, John Dickerson

Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc.

reinforcement-learning Reinforcement Learning (RL)

Repairing Regressors for Fair Binary Classification at Any Decision Threshold

no code implementations14 Mar 2022 Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai, John Dickerson, Keegan Hines, Suresh Venkatasubramanian

We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds.

Binary Classification Classification +1

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.

BIG-bench Machine Learning counterfactual

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

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 object-detection +2

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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