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We introduce several new black-box reductions that significantly improve the design of adaptive and parameter-free online learning algorithms by simplifying analysis, improving regret guarantees, and sometimes even improving runtime.
Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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A second-order Perceptron algorithm
Nicolò Cesa-Bianchi, Alex Conconi, and Claudio Gentile · 2005
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Prediction, learning, and games
Nicolò Cesa-Bianchi and Gábor Lugosi · 2006
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Convexity and well-posed problems
Roberto Lucchetti · 2006
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Biorthogonal systems in Banach spaces
Petr Hájek, Vicente Montesinos Santalucía, Jon Vanderwerff, and Václav Zizler · 2007
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Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, and Satyen Kale · 2007
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Online learning: Theory, algorithms, and applications
Shai Shalev-Shwartz · 2007
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Adaptive online gradient descent
Elad Hazan, Alexander Rakhlin, and Peter L Bartlett · 2008
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A parameter-free hedging algorithm
Kamalika Chaudhuri, Yoav Freund, and Daniel J Hsu · 2009
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Smoothness, low noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
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Less regret via online conditioning
Matthew Streeter and H Brendan McMahan · 2010
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On the universality of online mirror descent
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2011
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No-regret algorithms for unconstrained online convex optimization
Brendan McMahan and Matthew Streeter · 2012
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Dimension-free exponentiated gradient
Francesco Orabona · 2013
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Unconstrained online linear learning in hilbert spaces: Minimax algorithms and normal approximations
H Brendan McMahan and Francesco Orabona · 2014
Cited alongside, same era.
Simultaneous model selection and optimization through parameter-free stochastic learning
Francesco Orabona · 2014
Coin betting and parameter-free online learning
Francesco Orabona and Dávid Pál · 2016
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MetaGrad: Multiple learning rates in online learning
Tim van Erven and Wouter M Koolen · 2016
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Online auctions and multi-scale online learning
Sébastien Bubeck, Nikhil R Devanur, Zhiyi Huang, and Rad Niazadeh · 2017
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Online learning without prior information
Ashok Cutkosky and Kwabena Boahen · 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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Affine-invariant online optimization and the low-rank experts problem
Tomer Koren and Roi Livni · 2017
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Cited alongside, same era.
Adaptive online learning
Dylan J Foster, Alexander Rakhlin, and Karthik Sridharan · 2015
Cited alongside, same era.
Rosenthal-type inequalities for martingales in 2-smooth banach spaces
Iosif Pinelis · 2015
Cited alongside, same era.
Online convex optimization with unconstrained domains and losses
Ashok Cutkosky and Kwabena A Boahen · 2016
Cited alongside, same era.
Efficient second order online learning by sketching
Haipeng Luo, Alekh Agarwal, Nicolo Cesa-Bianchi, and John Langford · 2016
Cited alongside, same era.
Stochastic and adversarial online learning without hyperparameters
Ashok Cutkosky and Kwabena A Boahen
Cited in the paper.
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Scale-invariant unconstrained online learning
Wojciech Kotłowski · 2017
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A survey of algorithms and analysis for adaptive online learning
H Brendan McMahan · 2017
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Training deep networks without learning rates through coin betting
Francesco Orabona and Tatiana Tommasi · 2017
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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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