New differentially private iterative hard thresholding algorithms for high-dimensional sparse linear regression with heavy-tailed responses, with a claimed bound for the l1 variant that does not depend on the tail index.
In: Advances in Neural Information Processing Sys- tems (2019)
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Differentially Private Sparse Linear Regression with Heavy-tailed Responses
New differentially private iterative hard thresholding algorithms for high-dimensional sparse linear regression with heavy-tailed responses, with a claimed bound for the l1 variant that does not depend on the tail index.