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Regret minimization is treated as the golden rule in the traditional study of online learning.
The weighted majority algorithm
Nick Littlestone and Manfred K. Warmuth · 1994
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Tracking the best expert
Mark Herbster and Manfred K. Warmuth · 1998
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Prediction, Learning, and Games
Nicolò Cesa-Bianchi and Gábor Lugosi · 2006
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Adaptive algorithms for online decision problems
Elad Hazan and C. Seshadhri · 2007
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Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, and Satyen Kale · 2007
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Pegasos: primal estimated sub-gradient solver for SVM
Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro · 2007
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Optimal stragies and minimax lower bounds for online convex games
Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, and Ambuj Tewari · 2008
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Adaptive online gradient descent
Peter L. Bartlett, Elad Hazan, and Alexander Rakhlin · 2008
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Efficient learning algorithms for changing environments
Elad Hazan and C. Seshadhri · 2009
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Smoothness, low-noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2011
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A closer look at adaptive regret
Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov, and Vladimir Vovk · 2012
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Non-stationary stochastic optimization
Omar Besbes, Yonatan Gur, and Assaf Zeevi · 2015
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Strongly adaptive online learning
Amit Daniely, Alon Gonen, and Shai Shalev-Shwartz · 2015
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Online optimization: Competing with dynamic comparators
Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, and Karthik Sridharan · 2015
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Achieving all with no parameters: Adanormalhedge
Haipeng Luo and Robert E. Schapire · 2015
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Introduction to online convex optimization
Elad Hazan · 2016
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Online optimization in dynamic environments: Improved regret rates for strongly convex problems
Aryan Mokhtari, Shahin Shahrampour, Ali Jadbabaie, and Alejandro Ribeiro · 2016
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Mirror descent meets fixed share (and feels no regret)
Nicolò Cesa-bianchi, Pierre Gaillard, Gabor Lugosi, and Gilles Stoltz · 2012
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Efficient tracking of large classes of experts
András György, Tamás Linder, and Gábor Lugosi · 2012
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Dynamical models and tracking regret in online convex programming
Eric C. Hall and Rebecca M. Willett · 2013
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Improved strongly adaptive online learning using coin betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright, and Rebecca Willett
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Online learning for changing environments using coin betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright, and Rebecca Willett
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Adaptive online learning in dynamic environments
Lijun Zhang, Shiyin Lu, and Zhi-Hua Zhou
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Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient
Tianbao Yang, Lijun Zhang, Rong Jin, and Jinfeng Yi · 2016
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Improved dynamic regret for non-degenerate functions
Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, and Zhi-Hua Zhou · 2017
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Adaptive regret of convex and smooth functions
Lijun Zhang, Tie-Yan Liu, and Zhi-Hua Zhou · 2019
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