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Online Robust Mean Estimation
We note that if the algorithm is allowed to wait until time $T$ to report its estimate, this reduces to the well-studied problem of robust mean estimation.
Differentially Private Deep Learning with ModelMix
We also introduce a refined gradient clipping method, which can further sharpen the privacy loss in private learning when combined with ModelMix.
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
To better understand the challenges arising from irregular data distribution, in this paper we provide the first study on the problem of DP-SCO with heavy-tailed data in the high dimensional space.
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
For this case, we propose a method based on the sample-and-aggregate framework, which has an excess population risk of $\tilde{O}(\frac{d^3}{n\epsilon^4})$ (after omitting other factors), where $n$ is the sample size and $d$ is the dimensionality of the data.
Local Differential Privacy in Decentralized Optimization
Privacy concerns with sensitive data are receiving increasing attention.
Statistical Robust Chinese Remainder Theorem for Multiple Numbers: Wrapped Gaussian Mixture Model
Generalized Chinese Remainder Theorem (CRT) has been shown to be a powerful approach to solve the ambiguity resolution problem.
