13 citations · 27 across the 9 of their papers we have counts for
9 papers
Improved Differentially Private Regression via Gradient Boosting
Shuai Tang, Sergul Aydore, Michael Kearns +5
We revisit the problem of differentially private squared error linear regression. We observe that existing state-of-the-art methods are sensitive to the choice of hyperparameters -…
Federated Learning as a Network Effects Game
Shengyuan Hu, Dung Daniel Ngo, Shuran Zheng +2
Federated Learning (FL) aims to foster collaboration among a population of clients to improve the accuracy of machine learning without directly sharing local data. Although there h…
Counterfactual Prediction Under Outcome Measurement Error
Luke Guerdan, Amanda Coston, Kenneth Holstein +1
Across domains such as medicine, employment, and criminal justice, predictive models often target labels that imperfectly reflect the outcomes of interest to experts and policymake…
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making
Luke Guerdan, Amanda Coston, Zhiwei Steven Wu +1
A growing literature on human-AI decision-making investigates strategies for combining human judgment with statistical models to improve decision-making. Research in this area ofte…
Game-Theoretic Algorithms for Conditional Moment Matching
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell +1
A variety of problems in econometrics and machine learning, including instrumental variable regression and Bellman residual minimization, can be formulated as satisfying a set of c…
Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions"
Logan Stapleton, Hao-Fei Cheng, Anna Kawakami +7
This is an extended analysis of our paper "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions," which looks at racial disparities in the Allegheny Family…