419 citations · 3.1k across the 71 of their papers we have counts for
6 papers · 1 filter
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…
Faster SGD Using Sketched Conditioning
Alon Gonen, Shai Shalev-Shwartz
We propose a novel method for speeding up stochastic optimization algorithms via sketching methods, which recently became a powerful tool for accelerating algorithms for numerical…
On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems
Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir
We develop a novel framework to study smooth and strongly convex optimization algorithms, both deterministic and stochastic. Focusing on quadratic functions we are able to examine…
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…
SDCA without Duality
Shai Shalev-Shwartz
Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. In this paper we show how a variant of SDCA can be ap…
Strongly Adaptive Online Learning
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms t…