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To deal with changing environments, a new performance measure -- adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning.
The weighted majority algorithm
Nick Littlestone and Manfred K. Warmuth · 1994
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Using and combining predictors that specialize
Yoav Freund, Robert E. Schapire, Yoram Singer, and Manfred K. Warmuth · 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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Improved second-order bounds for prediction with expert advice
Nicolò Cesa-Bianchi, Yishay Mansour, and Gilles Stoltz · 2005
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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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Prediction with expert evaluators’ advice
Alexey Chernov and Vladimir Vovk · 2009
Cited alongside, same era.
Proximal regularization for online and batch learning
Chuong Do, Quoc Le, and Chuan-Sheng Foo · 2009
Cited alongside, same era.
Efficient learning algorithms for changing environments
Elad Hazan and C. Seshadhri · 2009
Cited alongside, same era.
Smoothness, low-noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
Cited alongside, same era.
Online learning and online convex optimization
Shai Shalev-Shwartz · 2011
Cited alongside, same era.
A closer look at adaptive regret
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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MetaGrad: Multiple learning rates in online learning
Tim van Erven and Wouter M Koolen · 2016
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Minimizing adaptive regret with one gradient per iteration
Guanghui Wang, Dakuan Zhao, and Lijun Zhang · 2018
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Dynamic regret of strongly adaptive methods
Lijun Zhang, Tianbao Yang, Rong Jin, and Zhi-Hua Zhou · 2018
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Lipschitz adaptivity with multiple learning rates in online learning
Zakaria Mhammedi, Wouter M Koolen, and Tim Van Erven · 2019
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Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov, and Vladimir Vovk · 2012
Cited alongside, same era.
The multiplicative weights update method: a meta-algorithm and applications
Sanjeev Arora, Elad Hazan, and Satyen Kale · 2012
Cited alongside, same era.
Efficient tracking of large classes of experts
András György, Tamás Linder, and Gábor Lugosi · 2012
Cited alongside, same era.
A second-order bound with excess losses
Pierre Gaillard, Gilles Stoltz, and Tim van Erven · 2014
Cited alongside, same era.
Strongly adaptive online learning
Amit Daniely, Alon Gonen, and Shai Shalev-Shwartz · 2015
Cited alongside, same era.
Improved strongly adaptive online learning using coin betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright, and Rebecca Willett
Cited in the paper.
Adaptivity and optimality: A universal algorithm for online convex optimization
Guanghui Wang, Shiyin Lu, and Lijun Zhang · 2019
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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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Parameter-free, dynamic, and strongly-adaptive online learning
Ashok Cutkosky · 2020
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Adapting to smoothness: A more universal algorithm for online convex optimization
Guanghui Wang, Shiyin Lu, Yao Hu, and Lijun Zhang · 2020
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