research
∙
04/23/2023
Robust and differentially private stochastic linear bandits
In this paper, we study the stochastic linear bandit problem under the a...
share
research
∙
05/31/2022
Communication-efficient distributed eigenspace estimation with arbitrary node failures
We develop an eigenspace estimation algorithm for distributed environmen...
research
∙
09/05/2020
Communication-efficient distributed eigenspace estimation
Distributed computing is a standard way to scale up machine learning and...
research
∙
02/19/2020
Entrywise convergence of iterative methods for eigenproblems
Several problems in machine learning, statistics, and other fields rely ...
research
∙
09/03/2019
Incrementally Updated Spectral Embeddings
Several fundamental tasks in data science rely on computing an extremal ...
research
∙
07/22/2019
Stochastic algorithms with geometric step decay converge linearly on sharp functions
Stochastic (sub)gradient methods require step size schedule tuning to pe...
research
∙
04/22/2019
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
The task of recovering a low-rank matrix from its noisy linear measureme...
research
∙
01/06/2019
Composite optimization for robust blind deconvolution
The blind deconvolution problem seeks to recover a pair of vectors from ...
research
∙
05/22/2018
