Gautam Kamath
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| title | conference | date |
|---|---|---|
| Privately Learning High-Dimensional Distributions | Data Privacy: From Foundations to Applications (Simons Institute workshop, 2019) | 2019-03-04 |
Claims a private algorithm that learns d‑dimensional Gaussians in total variation with sample complexity near d^2/α^2 plus d^2/(αε) and an extra… | ||
| Statistical Inference and Privacy, Part II | Data Privacy: Foundations and Applications Boot Camp | 2019-01-28 |
Differential privacy is applied to classical statistical tasks with concrete costs and fixes: identity testing needs Θ(√K/γ^2) samples nonprivately but privacy… | ||
| Realizing Robustness | Robust and High-Dimensional Statistics (Simons Institute workshop, 2018) | 2018-10-29 |
Claim: efficient algorithms achieve O(ε) error for mean (and covariance) estimation under ε-corruption with no dimension-dependent factor; method: compute… | ||
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