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Minimax Optimal Algorithms for Unconstrained Linear Optimization H. Brendan McMahan Google Reasearch Seattle, WA [removed]
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Document Date: 2014-11-25 16:31:21

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Paris / Work Regret / /

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Cambridge University Press / BT / p1T (12) T / Princeton University Press / /

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University of Pennsylvania / /

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online experts / online linear games / fundamental tool / minimax algorithm / online learning / minimax-optimal algorithms / online learning settings / online learning algorithms / on-line statistics / unconstrained online optimization / finance / online convex optimization framework / unconstrained optimization algorithms / unconstrained online convex optimization / bounded-loss hedging algorithm / minimax-optimal algorithm / give tools / No-regret algorithms / minimax optimal algorithm / no-regret algorithm / portfolio management / online algorithm / Online bandit learning / minimax optimal algorithms / computing / symmetric algorithm / classic algorithm / online linear game / /

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Pegasos / /

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Cambridge University / University of Michigan / Princeton University / the University of Pennsylvania / U.S. Securities and Exchange Commission / CIS Department / /

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Kelly Jr / Manfred K Warmuth / Wouter Koolen / Peter L. Bartlett / Peter DeMarzo / Ilan Kremer / Gilles Stoltz / Alexander Rakhlin / Sandy Zabell / Andrew Cotter / Ambuj Tewari / Alekh Agarwal / Gabor Lugosi / T . Then / V / Satyen Kale / Ohad Shamir / Rafael M. Frongillo / Robert E. Schapire / Yoram Singer / Raman Arora / Manfred K. Warmuth / Nathan Srebro / Andre Wibisono / Shai Shalev-Shwartz / Karthik Sridharan / Ofer Dekel / Cesa-Bianchi Lugosi / Joel Yellin / Matthew Streeter / J. L. Kelly Jr. / Jacob Abernethy / Amit Agarwal / Dmitry Adamskiy / Yishay Mansour / H. Brendan McMahan / Persi Diaconis / Elad Hazan / Ralph T. Rockafellar / Martin Zinkevich / H. Brendan McMahan Google Reasearch Seattle / /

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General / player / author / unconstrained player / forward / a minimax strategy / Singer / /

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Theorem 3 / /

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Michigan / Pennsylvania / /

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Machine Learning / /

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