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03/07/2023
PRIMO: Private Regression in Multiple Outcomes
We introduce a new differentially private regression setting we call Pri...
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03/03/2023
Model Explanation Disparities as a Fairness Diagnostic
In recent years, there has been a flurry of research focusing on the fai...
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11/10/2022
On the Privacy Risks of Algorithmic Recourse
As predictive models are increasingly being employed to make consequenti...
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06/08/2021
Adaptive Machine Unlearning
Data deletion algorithms aim to remove the influence of deleted data poi...
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07/06/2020
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
We study the data deletion problem for convex models. By leveraging tech...
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12/12/2019
Optimal, Truthful, and Private Securities Lending
We consider a fundamental dynamic allocation problem motivated by the pr...
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09/09/2019
A New Analysis of Differential Privacy's Generalization Guarantees
We give a new proof of the "transfer theorem" underlying adaptive data a...
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09/03/2019
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity
One of the most effective algorithms for differentially private learning...
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05/25/2019
Eliciting and Enforcing Subjective Individual Fairness
We revisit the notion of individual fairness first proposed by Dwork et ...
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04/07/2019
The Role of Interactivity in Local Differential Privacy
We study the power of interactivity in local differential privacy. First...
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11/19/2018
How to Use Heuristics for Differential Privacy
We develop theory for using heuristics to solve computationally hard pro...
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08/30/2018
Fair Algorithms for Learning in Allocation Problems
Settings such as lending and policing can be modeled by a centralized ag...
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08/24/2018
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Kearns et al. [2018] recently proposed a notion of rich subgroup fairnes...
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06/06/2018
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Data that is gathered adaptively --- via bandit algorithms, for example ...
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11/14/2017
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
The most prevalent notions of fairness in machine learning are statistic...
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06/07/2017