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A Reliable Effective Terascale Linear Learning System Alekh Agarwal Olivier Chapelle Department of EECS
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Document Date: 2012-02-13 20:25:22

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NY Yahoo! Research New York / MIT Press / Yahoo! / LG / Neural Information Processing Systems / Yahoo! Research New York / Prentice-Hall Inc. / CIKM / /

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University of California / K. Hall / Hall et al. / /

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Reliable Effective Terascale Linear Learning System Alekh Agarwal Olivier Chapelle Department of EECS UC Berkeley Yahoo! Research Santa Clara / MIT / University of California / Berkeley / North American Chapter / Association for Computational Linguistics / EECS Department / /

Person

John Duchi / Scott Shenker / Ron Bekkerman / John Langford / Jorge Nocedal / Alekh Agarwal / Joseph Gonzalez / Dennis DeCoste / Markus Weimer / Ion Stoica / Olivier Chapelle / Yoram Singer / Joseph M. Hellerstein / Zhaohui Zheng / Ankur Dave / Large Scale / Large Scale Kernel / Michael Kearns / Michael Franklin / Jeffrey Dean / Aapo Kyrola / Martin Zinkevich / Sanjay Ghemawat / Justin Ma / Jerry Ye / Jason Weston / Jyh-Herng Chow / Alex Smola / Jiang Chen / Murphy McCauley / Lihong Li / Yucheng Low / Misha Bilenko / Danny Bickson / Carlos Guestrin / Elad Hazan / Konrad Rieck / Miroslav Dudík / /

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programmer / Dean / /

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AllReduce / This solution / /

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California / /

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the Theory of Computing / Computational Linguistics / Machine Learning / the AllReduce times / Journal of Machine Learning Research / /

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existing learning algorithms / typical optimization algorithms / iterative machine learning algorithms / clearly 3 http /

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