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Jason Ge

2 accepted papers

2018

Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems

AISTATS 2018poster

This paper proposes a distributed privacy-preserving sparse PCA (DPS-PCA) algorithm that generates a minimax-optimal sparse PCA estimator under differential privacy constraints. In a distributed optimization framework, data providers can use this algorithm to collaboratively analyze the union of the…

Cited by 0Source ↗PDF ↗Scholar ↗
2017

On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning

NeurIPS 2017poster

We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions. Our proposed algorithm integrates the proximal newton algorithm with multi-stage convex relaxation based on the difference of convex (DC) programming, and enjoys both strong comp…

Cited by 16Source ↗PDF ↗Scholar ↗