21 citations · 46 across the 39 of their papers we have counts for
7 papers · 1 filter
Linear Last-Iterate Convergence Rates for Optimistic Learning in Harmonic Games
John Lazarsfeld, Anas Barakat, Jacob Abernethy +4
We prove that the last iterate of Optimistic Follow-The-Regularized-Leader (OFTRL) converges at a linear rate to a Nash equilibrium in multiplayer harmonic games. Our result holds…
The Role of Causality in Algorithmic Recourse
Srikanth Avasarala, Varun Gupta, Shahin Jabbari +2
Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage appli…
Effort Matters in Score-Based Admissions: How Retaking and Aggregation Shape Test Scores
Christine Ling, Diptangshu Sen, Juba Ziani
Observed standardized test scores are the result of an endogenous process: students strategically allocate effort across multiple retake attempts to improve their outcomes. Because…
The Impact of Competition on Outcomes of Score-Based College Admissions
George Bentley, Diptangshu Sen, Juba Ziani
We study how the design of admissions policies affects the ability of students admitted to universities. In our model, applicants have a multi-dimensional ability, which is a combi…
Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost
Madhura Pathegama, Srikanth Avasarala, Viveck R. Cadambe +1
We study privately estimating the sum of user-held values in the presence of an honest-but-curious server. This motivates requiring privacy not only at data release but also th…
Limits of Personalizing Differential Privacy Budgets
Edwige Cyffers, Juba Ziani
A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natural and well-studied workaround…
Data Sharing with Endogenous Choices over Differential Privacy Levels
Raef Bassily, Kate Donahue, Diptangshu Sen +2
Motivated by the rapid push to decentralize sharing of data, we study whether large-scale data sharing coalitions can form in a decentralized manner under differential privacy when…