Measuring Re-identification Risk

3 code implementations • 12 Apr 2023 • CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong

In this work, we present a new theoretical framework to measure re-identification risk in such user representations.

Stars: Tera-Scale Graph Building for Clustering and Graph Learning

no code implementations • 5 Dec 2022 • CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong

We evaluate the performance of Stars for clustering and graph learning, and demonstrate 10~1000-fold improvements in pairwise similarity comparisons compared to different baselines, and 2~10-fold improvement in running time without quality loss.

Clustering Graph Learning

metric-learn: Metric Learning Algorithms in Python

6 code implementations • 13 Aug 2019 • William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet

metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms.

BIG-bench Machine Learning Metric Learning +1