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stat.ML2026
Inference of Online Newton Methods with Nesterov's Accelerated Sketching
Haoxuan Wang, Xinchen Du, Sen Na
Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order methods enable efficient iterate updates, their inf…
stat.ML2026
Online Covariance Matrix Estimation in Sketched Newton Methods
Wei Kuang, Mihai Anitescu, Sen Na
Given the ubiquity of streaming data, online algorithms have been widely used for parameter estimation, with second-order methods particularly standing out for their efficiency and…
stat.ML2025
Online Covariance Estimation in Nonsmooth Stochastic Approximation
Liwei Jiang, Abhishek Roy, Krishna Balasubramanian +3
We consider applying stochastic approximation (SA) methods to solve nonsmooth variational inclusion problems. Existing studies have shown that the averaged iterates of SA methods e…