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To cope with changing environments, recent developments in online learning have introduced the concepts of adaptive regret and dynamic regret independently.
The weighted majority algorithm
Nick Littlestone and Manfred K. Warmuth · 1994
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Tracking the best expert
Mark Herbster and Manfred K. Warmuth · 1998
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Tracking the best linear predictor
Mark Herbster and Manfred K. Warmuth · 2001
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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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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A primal-dual perspective of online learning algorithms
Shai Shalev-Shwartz and Yoram Singer · 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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A stochastic view of optimal regret through minimax duality
Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, and Alexander Rakhlin · 2009
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Efficient learning algorithms for changing environments
Elad Hazan and C. Seshadhri · 2009
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Sparse online learning via truncated gradient
John Langford, Lihong Li, and Tong Zhang · 2009
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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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Mirror descent meets fixed share (and feels no regret)
Nicolò Cesa-bianchi, Pierre Gaillard, Gabor Lugosi, and Gilles Stoltz · 2012
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Online kernel learning with a near optimal sparsity bound
Lijun Zhang, Jinfeng Yi, Rong Jin, Ming Lin, and Xiaofei He · 2013
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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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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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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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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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Open problem: Fast stochastic exp-concave optimization
Tomer Koren · 2013
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Improved Strongly Adaptive Online Learning using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright, and Rebecca Willett · 2017
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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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Minimizing adaptive regret with one gradient per iteration
Guanghui Wang, Dakuan Zhao, and Lijun Zhang · 2018
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