Associate Professor, CMU, Carnegie Mellon University
3 papers at NeurIPS 2025
We prove that Private Evolution, a practical algorithm for differentially private synthetic data generation, converges in Wasserstein distance as the number of samples increases under a new, realistic theoretical model
