381 citations · 733 across the 13 of their papers we have counts for
20 papers
GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
Anupama Nandi, Seth Neel, Hyunghoon Cho
Genome-wide association studies (GWAS) are an essential tool in biomedical research for identifying genetic factors linked to health and disease. However, publicly releasing GWAS s…
PRIMO: Private Regression in Multiple Outcomes
Seth Neel
We introduce a new private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired by the common situation where a data analyst wants to perform a set…
Feature Importance Disparities for Data Bias Investigations
Peter W. Chang, Leor Fishman, Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as…
On the Privacy Risks of Algorithmic Recourse
Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to…
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…
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…