تقرير
Agnostic Private Density Estimation via Stable List Decoding
| العنوان: | Agnostic Private Density Estimation via Stable List Decoding |
|---|---|
| المؤلفون: | Afzali, Mohammad, Ashtiani, Hassan, Liaw, Christopher |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Data Structures and Algorithms, Computer Science - Information Theory, Computer Science - Machine Learning |
| الوصف: | We introduce a new notion of stability--which we call stable list decoding--and demonstrate its applicability in designing differentially private density estimators. This definition is weaker than global stability [ABLMM22] and is related to the notions of replicability [ILPS22] and list replicability [CMY23]. We show that if a class of distributions is stable list decodable, then it can be learned privately in the agnostic setting. As the main application of our framework, we prove the first upper bound on the sample complexity of private density estimation for Gaussian Mixture Models in the agnostic setting, extending the realizable result of Afzali et al. [AAL24]. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.04783 |
| رقم الأكسشن: | edsarx.2407.04783 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |