
Adaptive Machine Unlearning
Data deletion algorithms aim to remove the influence of deleted data poi...
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Rejoinder: Gaussian Differential Privacy
In this rejoinder, we aim to address two broad issues that cover most co...
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Differentially Private Query Release Through Adaptive Projection
We propose, implement, and evaluate a new algorithm for releasing answer...
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Lexicographically Fair Learning: Algorithms and Generalization
We extend the notion of minimax fairness in supervised learning problems...
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Best vs. All: Equity and Accuracy of Standardized Test Score Reporting
We study a game theoretic model of standardized testing for college admi...
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Online Multivalid Learning: Means, Moments, and Prediction Intervals
We present a general, efficient technique for providing contextual predi...
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Convergent Algorithms for (Relaxed) Minimax Fairness
We consider a recently introduced framework in which fairness is measure...
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Testing Differential Privacy with Dual Interpreters
Applying differential privacy at scale requires convenient ways to check...
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Moment Multicalibration for Uncertainty Estimation
We show how to achieve the notion of "multicalibration" from HébertJohn...
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DescenttoDelete: GradientBased Methods for Machine Unlearning
We study the data deletion problem for convex models. By leveraging tech...
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Algorithms and Learning for Fair Portfolio Design
We consider a variation on the classical finance problem of optimal port...
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Fair Prediction with Endogenous Behavior
There is increasing regulatory interest in whether machine learning algo...
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Pipeline Interventions
We introduce the pipeline intervention problem, defined by a layered dir...
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Differentially Private Call Auctions and Market Impact
We propose and analyze differentially private (DP) mechanisms for call a...
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Guidelines for Implementing and Auditing Differentially Private Systems
Differential privacy is an information theoretic constraint on algorithm...
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Optimal, Truthful, and Private Securities Lending
We consider a fundamental dynamic allocation problem motivated by the pr...
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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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Differentially Private Objective Perturbation: Beyond Smoothness and Convexity
One of the most effective algorithms for differentially private learning...
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Exponential Separations in Local Differential Privacy Through Communication Complexity
We prove a general connection between the communication complexity of tw...
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Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
We design a general framework for answering adaptive statistical queries...
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Fuzzi: A ThreeLevel Logic for Differential Privacy
Curators of sensitive datasets sometimes need to know whether queries ag...
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Eliciting and Enforcing Subjective Individual Fairness
We revisit the notion of individual fairness first proposed by Dwork et ...
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Average Individual Fairness: Algorithms, Generalization and Experiments
We propose a new family of fairness definitions for classification probl...
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Gaussian Differential Privacy
Differential privacy has seen remarkable success as a rigorous and pract...
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The Role of Interactivity in Local Differential Privacy
We study the power of interactivity in local differential privacy. First...
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Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which...
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Differentially Private Fair Learning
We design two learning algorithms that simultaneously promise differenti...
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How to Use Heuristics for Differential Privacy
We develop theory for using heuristics to solve computationally hard pro...
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The Frontiers of Fairness in Machine Learning
The last few years have seen an explosion of academic and popular intere...
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Fair Algorithms for Learning in Allocation Problems
Settings such as lending and policing can be modeled by a centralized ag...
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Downstream Effects of Affirmative Action
We study a twostage model, in which students are 1) admitted to college...
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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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Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Data that is gathered adaptively  via bandit algorithms, for example ...
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Local Differential Privacy for Evolving Data
There are now several large scale deployments of differential privacy us...
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Online Learning with an Unknown Fairness Metric
We consider the problem of online learning in the linear contextual band...
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A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Bandit learning is characterized by the tension between longterm explor...
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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
The most prevalent notions of fairness in machine learning are statistic...
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Strategic Classification from Revealed Preferences
We study an online linear classification problem, in which the data is g...
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A Convex Framework for Fair Regression
We introduce a flexible family of fairness regularizers for (linear and ...
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Fairness in Criminal Justice Risk Assessments: The State of the Art
Objectives: Discussions of fairness in criminal justice risk assessments...
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Fairness in Learning: Classic and Contextual Bandits
We introduce the study of fairness in multiarmed bandit problems. Our f...
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Aaron Roth
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