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We study online learnability of a wide class of problems, extending the results of (Rakhlin, Sridharan, Tewari, 2010) to general notions of performance measure well beyond external regret.
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Asymptotic calibration
D.P. Foster and R.V. Vohra · 1998
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Tracking the best expert
M. Herbster and M. K. Warmuth · 1998
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The importance of convexity in learning with squared loss
W. S. Lee, P. L. Bartlett, and R. C. Williamson · 1998
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Tracking a small set of experts by mixing past posteriors
O. Bousquet and M. K. Warmuth · 2002
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Approachability in infinite dimensional spaces
E. Lehrer · 2003
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Online convex programming and generalized infinitesimal gradient ascent
M. Zinkevich · 2003
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Learning correlated equilibria in games with compact sets of strategies
G. Stoltz and G. Lugosi · 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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No-regret learning in convex games
G.J. Gordon, A. Greenwald, and C. Marks · 2008
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A stochastic view of optimal regret through minimax duality
J. Abernethy, A. Agarwal, P. Bartlett, and A. Rakhlin · 2009
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Agnostic online learning
S. Ben-David, D. Pal, and S. Shalev-Shwartz · 2009
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Online learning for global cost functions
E. Even-Dar, R. Kleinberg, S. Mannor, and Y. Mansour · 2009
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From external to internal regret
A. Blum and Y. Mansour · 2005
Cited alongside, same era.
Prediction, Learning, and Games
N. Cesa-Bianchi and G. Lugosi · 2006
Cited alongside, same era.
Computational equivalence of fixed points and no regret algorithms, and convergence to equilibria
E. Hazan and S. Kale · 2007
Cited alongside, same era.
Efficient learning algorithms for changing environments
E. Hazan and C. Seshadhri · 2009
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A geometric proof of calibration
S. Mannor and G. Stoltz · 2009
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Online learning: Random averages, combinatorial parameters, and learnability
A. Rakhlin, K. Sridharan, and A. Tewari · 2010
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