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We show a principled way of deriving online learning algorithms from a minimax analysis.
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
N. Littlestone · 1988
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Empirical margin distributions and bounding the generalization error of combined classifiers
V. Koltchinskii and D. Panchenko · 2002
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Local rademacher complexities
P.L. Bartlett, O. Bousquet, and S. Mendelson · 2005
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Efficient algorithms for online decision problems
A. Kalai and S. Vempala · 2005
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Prediction, Learning, and Games
N. Cesa-Bianchi and G. Lugosi · 2006
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Adaptive online gradient descent
P.L. Bartlett, E. Hazan, and A. Rakhlin · 2007
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Optimal strategies and minimax lower bounds for online convex games
J. Abernethy, P. L. Bartlett, A. Rakhlin, and A. Tewari · 2008
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Optimal strategies from random walks
J. Abernethy, M.K. Warmuth, and J. Yellin · 2008
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A stochastic view of optimal regret through minimax duality
J. Abernethy, A. Agarwal, P. L. Bartlett, and A. Rakhlin · 2009
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Agnostic online learning
S. Ben-David, D. Pál, and S. Shalev-Shwartz · 2009
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A parameter-free hedging algorithm
K. Chaudhuri, Y. Freund, and D. Hsu · 2009
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Random walk approach to regret minimization
H. Narayanan and A. Rakhlin · 2010
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Online learning: Random averages, combinatorial parameters, and learnability
A. Rakhlin, K. Sridharan, and A. Tewari · 2010
Efficient online learning via randomized rounding
N. Cesa-Bianchi and O. Shamir · 2011
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Online learning: Beyond regret
A. Rakhlin, K. Sridharan, and A. Tewari · 2011
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Online learning: Stochastic, constrained, and smoothed adversaries
A. Rakhlin, K. Sridharan, and A. Tewari · 2011
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Collaborative filtering with the trace norm: Learning, bounding, and transducing
O. Shamir and S. Shalev-Shwartz · 2011
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On the universality of online mirror descent
Nati Srebro, Karthik Sridharan, and Ambuj Tewari · 2011
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Convex games in banach spaces
K. Sridharan and A. Tewari · 2010
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T. van Erven, P. Grünwald, W. M. Koolen, and S. de Rooij · 2011
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