30 citations · 102 across the 35 of their papers we have counts for
7 papers · 1 filter
Matrix-Free Preconditioning in Online Learning
Ashok Cutkosky, Tamas Sarlos
We provide an online convex optimization algorithm with regret that interpolates between the regret of an algorithm using an optimal preconditioning matrix and one using a diagonal…
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
In this paper, we consider the nonparametric least square regression in a Reproducing Kernel Hilbert Space (RKHS). We propose a new randomized algorithm that has optimal generaliza…
Momentum-Based Variance Reduction in Non-Convex SGD
Ashok Cutkosky, Francesco Orabona
Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the conv…
Anytime Online-to-Batch Conversions, Optimism, and Acceleration
Ashok Cutkosky
A standard way to obtain convergence guarantees in stochastic convex optimization is to run an online learning algorithm and then output the average of its iterates: the actual ite…
Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning
Ashok Cutkosky
We provide algorithms that guarantee regret or for online conve…
Combining Online Learning Guarantees
Ashok Cutkosky
We show how to take any two parameter-free online learning algorithms with different regret guarantees and obtain a single algorithm whose regret is the minimum of the two base alg…