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This paper considers the stability of online learning algorithms and its implications for learnability (bounded regret).
The relaxation method of finding the common points of convex sets and its appli- cation to the solution of problems in convex programming
L. M. Bregman · 1967
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Problem Complexity and Method Efficiency in Optimization
Arkadi Nemirovski and D Yudin · 1983
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Parallel Optimization: Theory, Algorithms, and Applications
Y. Censor and S. Zenios · 1998
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Statistical Learning Theory
V. Vapnik · 1998
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Stability and generalization
Olivier Bousquet and Andre Elisseeff · 2000
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Almost-everywhere algorithmic stability and generalization error
Samuel Kutin and Partha Niyogi · 2002
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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Stability results in learning theory
Alexander Rakhlin, Sayan Mukherjee, and Tomaso Poggio · 2005
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Prediction, Learning, and Games
Nicolo Cesa-Bianchi and Gabor Lugosi · 2006
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The tradeoffs of large scale learning
L. Bottou and O. Bousquet · 2007
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Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, and Satyen Kale · 2007
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Mind the duality gap: Logarithmic regret algorithms for online optimization
Shai Shalev-Shwartz and Sham M. Kakade · 2008
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Beating the adaptive bandit with high probability
Jacob Abernethy and Alexander Rakhlin · 2009
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Efficient online and batch learning using forward backward splitting
John Duchi and Yoram Singer · 2009
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Lecture notes on online learning, 2009
Alexander Rakhlin · 2009
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Online learning: Random averages, combinatorial parameters, and learnability
Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari · 2010
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Learnability, stability and uniform convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2010
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Dual averaging methods for regularized stochastic learning and online optimization
Lin Xiao · 2010
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The generalization ability of online algorithms for dependent data
Alekh Agarwal and John C. Duchi · 2011
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Differentially private online learning
Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta · 2011
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Composite objective mirror descent
John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Ambuj Tewari · 2010
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Implicit online learning
Brian Kulis and Peter L. Bartlett · 2010
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Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization
H. Brendan McMahan · 2011
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Online learning, stability, and stochastic gradient descent
Tomaso Poggio, Stephen Voinea, and Lorenzo Rosasco · 2011
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Stability conditions for online learnability
Stéphane Ross and J. Andrew Bagnell · 2011
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