194 citations · 325 across the 5 of their papers we have counts for
12 papers · 1 filter
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…
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…
Oracle Efficient Private Non-Convex Optimization
Seth Neel, Aaron Roth, Giuseppe Vietri +1
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deri…
An Algorithmic Framework for Fairness Elicitation
Christopher Jung, Michael Kearns, Seth Neel +3
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more com…
The Role of Interactivity in Local Differential Privacy
Matthew Joseph, Jieming Mao, Seth Neel +1
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially…