6 papers
One-Shot Private Confidence Regions via Resampling
Shourya Pandey, Purnamrita Sarkar, Po-Ling Loh +1
We propose a simple framework for constructing differentially private confidence regions \textit{in one shot}, i.e., by adding noise only to the final resampling quantile instead o…
On the Curse of Dimensionality in Private Sparse Covariance Estimation and PCA
Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar +1
We study high-dimensional differentially private (DP) covariance estimation in the operator norm, and principal component analysis (PCA), under -row-column sparsity (-RCS) of…
Low-Precision Streaming PCA
Sanjoy Dasgupta, Syamantak Kumar, Shourya Pandey +1
Low-precision streaming PCA estimates the top principal component in a streaming setting under limited precision. We establish an information-theoretic lower bound on the quantizat…
Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA
Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar
We propose a novel statistical inference framework for streaming principal component analysis (PCA) using Oja's algorithm, enabling the construction of confidence intervals for ind…
On Differentially Private U Statistics
Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey +1
We consider the problem of privately estimating a parameter , where , , , are i.i.d. data from some distribution and is a p…
Black-Box -to--PCA Reductions: Theory and Applications
Arun Jambulapati, Syamantak Kumar, Jerry Li +3
The -principal component analysis (-PCA) problem is a fundamental algorithmic primitive that is widely-used in data analysis and dimensionality reduction applications. In sta…