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Found 4 papers, 1 papers with code
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization
This paper considers the generalization performance of differentially private convex learning.
Efficient Convex Relaxations for Streaming PCA
We revisit two algorithms, matrix stochastic gradient (MSG) and $\ell_2$-regularized MSG (RMSG), that are instances of stochastic gradient descent (SGD) on a convex relaxation to principal component analysis (PCA).
Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features
We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve $O(1/\epsilon^2)$ sample complexity.
Streaming Principal Component Analysis in Noisy Setting
We study streaming algorithms for principal component analysis (PCA) in noisy settings.
