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Found 19 papers, 4 papers with code
Fair Clustering: Critique, Caveats, and Future Directions
Clustering is a fundamental problem in machine learning and operations research.
Fair Polylog-Approximate Low-Cost Hierarchical Clustering
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.
Artificial Intelligence/Operations Research Workshop 2 Report Out
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.
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective
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
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
Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc.
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds.
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.
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers.
Learning Revenue-Maximizing Auctions With Differentiable Matching
We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations.
Counterfactual Explanations for Machine Learning: Challenges Revisited
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
Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike.
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.
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.
Robust Active Preference Elicitation
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.
Forming Diverse Teams from Sequentially Arriving People
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.
Adversarial Training for Free!
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Universal Adversarial Training
Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels.
