54 citations · 144 across the 7 of their papers we have counts for
12 papers
Minimax Group Fairness in Strategic Classification
Emily Diana, Saeed Sharifi-Malvajerdi, Ali Vakilian
In strategic classification, agents manipulate their features, at a cost, to receive a positive classification outcome from the learner's classifier. The goal of the learner in suc…
Bayesian Strategic Classification
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
In strategic classification, agents modify their features, at a cost, to ideally obtain a positive classification from the learner's classifier. The typical response of the learner…
Multiaccurate Proxies for Downstream Fairness
Emily Diana, Wesley Gill, Michael Kearns +3
We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time -- in other words, how can we…
Adaptive Machine Unlearning
Varun Gupta, Christopher Jung, Seth Neel +3
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for seq…
Lexicographically Fair Learning: Algorithms and Generalization
Emily Diana, Wesley Gill, Ira Globus-Harris +3
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short). Informally, given a…
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are…
Algorithms and Learning for Fair Portfolio Design
Emily Diana, Travis Dick, Hadi Elzayn +5
We consider a variation on the classical finance problem of optimal portfolio design. In our setting, a large population of consumers is drawn from some distribution over risk tole…
Differentially Private Call Auctions and Market Impact
Emily Diana, Hadi Elzayn, Michael Kearns +3
We propose and analyze differentially private (DP) mechanisms for call auctions as an alternative to the complex and ad-hoc privacy efforts that are common in modern electronic mar…
A New Analysis of Differential Privacy's Generalization Guarantees
Christopher Jung, Katrina Ligett, Seth Neel +3
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially priva…
Average Individual Fairness: Algorithms, Generalization and Experiments
Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
We propose a new family of fairness definitions for classification problems that combine some of the best properties of both statistical and individual notions of fairness. We posi…
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao +4
Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attri…
Analytical Studies of Fragmented-Spectrum Multi-Level OFDM-CDMA Technique in Cognitive Radio Networks
Farhad Akhoundi, Saeed Sharifi-Malvajerdi, Omid Poursaeed +1
In this paper, we present a multi-user resource allocation framework using fragmented-spectrum synchronous OFDM-CDMA modulation over a frequency-selective fading channel. In partic…