Models That Prove Their Own Correctness

no code implementations • 24 May 2024 • Noga Amit, Shafi Goldwasser, Orr Paradise, Guy Rothblum

The *soundness* property of $V$ guarantees that, for *every* input, no model can convince $V$ of the correctness of an incorrect output.

Samplable Anonymous Aggregation for Private Federated Data Analysis

no code implementations • 27 Jul 2023 • Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina, Vitaly Feldman, Bailey Basile, Aine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy Rothblum, Michael Scaria, Linmao Song, Congzheng Song, Karl Tarbe, Sebastian Vogt, Luke Winstrom, Shundong Zhou

We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data.

Federated Learning

Multicalibration: Calibration for the (Computationally-Identifiable) Masses

no code implementations • ICML 2018 • Ursula Hebert-Johnson, Michael Kim, Omer Reingold, Guy Rothblum

We develop and study multicalibration as a new measure of fairness in machine learning that aims to mitigate inadvertent or malicious discrimination that is introduced at training time (even from ground truth data).

Fairness