124 citations · 1.6k across the 112 of their papers we have counts for
7 papers · 1 filter
Fast and Simple PCA via Convex Optimization
Dan Garber, Elad Hazan
The problem of principle component analysis (PCA) is traditionally solved by spectral or algebraic methods. We show how computing the leading principal component could be reduced t…
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
Jacob Abernethy, Elad Hazan
This paper explores a surprising equivalence between two seemingly-distinct convex optimization methods. We show that simulated annealing, a well-studied random walk algorithms, is…
Beyond Convexity: Stochastic Quasi-Convex Optimization
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
Stochastic convex optimization is a basic and well studied primitive in machine learning. It is well known that convex and Lipschitz functions can be minimized efficiently using St…
Online Gradient Boosting
Alina Beygelzimer, Elad Hazan, Satyen Kale +1
We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm i…
The Computational Power of Optimization in Online Learning
Elad Hazan, Tomer Koren
We consider the fundamental problem of prediction with expert advice where the experts are "optimizable": there is a black-box optimization oracle that can be used to compute, in c…
On Graduated Optimization for Stochastic Non-Convex Problems
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
The graduated optimization approach, also known as the continuation method, is a popular heuristic to solving non-convex problems that has received renewed interest over the last d…