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We show how to take any two parameter-free online learning algorithms with different regret guarantees and obtain a single algorithm whose regret is the minimum of the two base algorithms.
Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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On the generalization ability of on-line learning algorithms
Nicolo Cesa-Bianchi, Alex Conconi, and Claudio Gentile · 2004
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Empirical bernstein bounds and sample variance penalization
Andreas Maurer and Massimiliano Pontil · 2009
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Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2010
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Extracting certainty from uncertainty: Regret bounded by variation in costs
Elad Hazan and Satyen Kale · 2010
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Adaptive bound optimization for online convex optimization
H. Brendan McMahan and Matthew Streeter · 2010
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2011
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Online optimization with gradual variations
Chao-Kai Chiang, Tianbao Yang, Chia-Jung Lee, Mehrdad Mahdavi, Chi-Jen Lu, Rong Jin, and Shenghuo Zhu · 2012
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No-regret algorithms for unconstrained online convex optimization
Brendan Mcmahan and Matthew Streeter · 2012
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Normalized online learning
Stephane Ross, Paul Mineiro, and John Langford · 2013
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Online learning with predictable sequences
Alexander Rakhlin and Karthik Sridharan · 2013
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Dimension-free exponentiated gradient
Francesco Orabona · 2013
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A survey of algorithms and analysis for adaptive online learning
H. Brendan McMahan · 2014
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Simultaneous model selection and optimization through parameter-free stochastic learning
Francesco Orabona · 2014
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On equivalence of martingale tail bounds and deterministic regret inequalities
Alexander Rakhlin and Karthik Sridharan · 2015
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Coin betting and parameter-free online learning
Francesco Orabona and Dávid Pál · 2016
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Accelerating online convex optimization via adaptive prediction
Mehryar Mohri and Scott Yang · 2016
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Training deep networks without learning rates through coin betting
Francesco Orabona and Tatiana Tommasi · 2017
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Parameter-free online learning via model selection
Dylan J Foster, Satyen Kale, Mehryar Mohri, and Karthik Sridharan · 2017
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Black-box reductions for parameter-free online learning in banach spaces
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Unconstrained online linear learning in hilbert spaces: Minimax algorithms and normal approximations
H Brendan McMahan and Francesco Orabona · 2014
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Adaptive online learning
Dylan J Foster, Alexander Rakhlin, and Karthik Sridharan · 2015
Cited alongside, same era.
Ashok Cutkosky and Francesco Orabona · 2018
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Online learning: Sufficient statistics and the burkholder method
Dylan J. Foster, Alexander Rakhlin, and Karthik Sridharan · 2018
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