36 citations · 133 across the 15 of their papers we have counts for
20 papers
Differentially Private Image Classification from Features
Harsh Mehta, Walid Krichene, Abhradeep Thakurta +2
Leveraging transfer learning has recently been shown to be an effective strategy for training large models with Differential Privacy (DP). Moreover, somewhat surprisingly, recent w…
Fully Adaptive Composition for Gaussian Differential Privacy
Adam Smith, Abhradeep Thakurta
We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a ful…
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search
Yannis Cattan, Christopher A. Choquette-Choo, Nicolas Papernot +1
Models need to be trained with privacy-preserving learning algorithms to prevent leakage of possibly sensitive information contained in their training data. However, canonical algo…
Large Scale Transfer Learning for Differentially Private Image Classification
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin +1
Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. In the field of deep learning, Differentially Priv…
Toward Training at ImageNet Scale with Differential Privacy
Alexey Kurakin, Shuang Song, Steve Chien +3
Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the tr…
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene +4
We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Sq…