High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
Summary: First study of high-dimensional DP-SCO under heavy-tailed data: derives high-probability excess-risk bounds for polytope constraints and an improved LASSO rate O~((log d)/(n ε)^2) under bounded fourth moments. Introduces truncated DP-HT for sparse learning achieving O~(s^2 log d/(n ε)) and an l0-constrained method with O~(s^{3/2} log d/(n ε)), nearly optimal up to √s. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Lijie Hu
- 2. Shuo Ni
- 3. Hanshen Xiao
- 4. Di Wang
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