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The diverse world of machine learning applications has given rise to a plethora of algorithms and optimization methods, finely tuned to the specific regression or classification task at hand. We reduce the complexity of algorithm design for machine learning by reductions: we develop reductions that take a method developed for one setting and apply it to the entire spectrum of smoothness and strong-convexity in applications. Furthermore, unlike existing results, our new reductions are OPTIMAL and more PRACTICAL. We show how these new reductions give rise to new and faster running times on training linear classifiers for various families of loss functions, and conclude with experiments showing their successes also in practice.
Author Information
Zeyuan Allen-Zhu (Princeton University)
Elad Hazan (Princeton University and Google Brain)
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2017 Poster: Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls »
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2017 Poster: Learning Linear Dynamical Systems via Spectral Filtering »
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2016 Poster: Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters »
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2016 Poster: Even Faster SVD Decomposition Yet Without Agonizing Pain »
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2015 Poster: Online Learning for Adversaries with Memory: Price of Past Mistakes »
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2015 Poster: Beyond Convexity: Stochastic Quasi-Convex Optimization »
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2009 Poster: On Stochastic and Worst-case Models for Investing »
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2009 Oral: On Stochastic and Worst-case Models for Investing »
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2009 Poster: An Efficient Interior-Point Method for Minimum-Regret Learning in Online Convex Optimization »
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