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20172022
most citedRethinking gradient sparsification as total error minimization

19 citations · 38 across the 9 of their papers we have counts for

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8 papers · 1 filter

math.OC2020

On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems

Atal Narayan Sahu, Aritra Dutta, Aashutosh Tiwari +1

In the realm of big data and machine learning, data-parallel, distributed stochastic algorithms have drawn significant attention in the present days.~While the synchronous versions…

math.OC2019

Direct Nonlinear Acceleration

Aritra Dutta, El Houcine Bergou, Yunming Xiao +2

Optimization acceleration techniques such as momentum play a key role in state-of-the-art machine learning algorithms. Recently, generic vector sequence extrapolation techniques, s…

math.OC2019

Best Pair Formulation & Accelerated Scheme for Non-convex Principal Component Pursuit

Aritra Dutta, Filip Hanzely, Jingwei Liang +1

The best pair problem aims to find a pair of points that minimize the distance between two disjoint sets. In this paper, we formulate the classical robust principal component analy…

math.OC2018

A Nonconvex Projection Method for Robust PCA

Aritra Dutta, Filip Hanzely, Peter Richtárik

Robust principal component analysis (RPCA) is a well-studied problem with the goal of decomposing a matrix into the sum of low-rank and sparse components. In this paper, we propose…

math.OC2017

Online and Batch Supervised Background Estimation via L1 Regression

Aritra Dutta, Peter Richtarik

We propose a surprisingly simple model for supervised video background estimation. Our model is based on regression. As existing methods for regression do not sca…

math.OC20174 cited

Weighted Low Rank Approximation for Background Estimation Problems

Aritra Dutta, Xin Li

Classical principal component analysis (PCA) is not robust to the presence of sparse outliers in the data. The use of the norm in the Robust PCA (RPCA) method successfully…