519 citations · 1.6k across the 49 of their papers we have counts for
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Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine learning. For strongly convex problems, its conver…
On the Universality of Online Mirror Descent
Nathan Srebro, Karthik Sridharan, Ambuj Tewari
We show that for a general class of convex online learning problems, Mirror Descent can always achieve a (nearly) optimal regret guarantee.
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Andrew Cotter, Ohad Shamir, Nathan Srebro +1
Mini-batch algorithms have been proposed as a way to speed-up stochastic convex optimization problems. We study how such algorithms can be improved using accelerated gradient metho…
Online Learning: Stochastic and Constrained Adversaries
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Learning theory has largely focused on two main learning scenarios. The first is the classical statistical setting where instances are drawn i.i.d. from a fixed distribution and th…