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20192021
most citedLeast Squares Regression with Markovian Data: Fundamental Limits and Algorithms

6 citations · 6 across the 2 of their papers we have counts for

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math.OC2019

Making the Last Iterate of SGD Information Theoretically Optimal

Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli

Stochastic gradient descent (SGD) is one of the most widely used algorithms for large scale optimization problems. While classical theoretical analysis of SGD for convex problems s…

math.OC2019

SGD without Replacement: Sharper Rates for General Smooth Convex Functions

Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli

We study stochastic gradient descent {\em without replacement} (\sgdwor) for smooth convex functions. \sgdwor is widely observed to converge faster than true \sgd where each sample…