2014
ICML
ICML 2014
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization
Abstract
We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results for various key machine learning optimization problems including SVM, logistic regression, ridge regression, Lasso, and multiclass SVM. Experiments validate our theoretical findings.
🌉
Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
🧭
Keyword Pioneer
— dual ascent
🐣
Hot Topic Early Bird
— stochastic optimization
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio