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Recent literature on online learning has focused on developing adaptive algorithms that take advantage of a regularity of the sequence of observations, yet retain worst-case performance guarantees.
A. S. Nemirovski and D. B. Yudin, Problem complexity and method efficiency in optimization . Wiley (Chichester and New York), 1983
1983
Earlier work this paper cites.
Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of computer and system sciences , vol. 55, no. 1, pp. 119–139, 1997
1997
Earlier work this paper cites.
M. Zinkevich, “Online convex programming and generalized infinitesimal gradient ascent,” in International Conference on Machine Learning , 2003
2003
Earlier work this paper cites.
O. Bousquet and M. K. Warmuth, “Tracking a small set of experts by mixing past posteriors,” The Journal of Machine Learning Research , vol. 3, pp. 363–396, 2003
2003
Earlier work this paper cites.
A. Beck and M. Teboulle, “Mirror descent and nonlinear projected subgradient methods for convex optimization,” Operations Research Letters , vol. 31, no. 3, pp. 167–175, 2003
2003
Earlier work this paper cites.
A. Nemirovski, “Prox-method with rate of convergence o (1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems,” SIAM Journal on Optimization , vol. 15, no. 1, pp. 229–251, 2004
2004
Earlier work this paper cites.
N. Cesa-Bianchi, G. Lugosi et al. , Prediction, learning, and games . Cambridge University Press Cambridge, 2006, vol. 1, no. 1.1
2006
Cited alongside, same era.
E. Hazan, A. Agarwal, and S. Kale, “Logarithmic regret algorithms for online convex optimization,” Machine Learning , vol. 69, no. 2-3, pp. 169–192, 2007
2007
Cited alongside, same era.
J. Abernethy, P. L. Bartlett, A. Rakhlin, and A. Tewari, “Optimal strategies and minimax lower bounds for online convex games,” in Proceedings of the Nineteenth Annual Conference on Computational Learning Theory , 2008
2008
Cited alongside, same era.
E. Hazan and S. Kale, “Extracting certainty from uncertainty: Regret bounded by variation in costs,” Machine learning , vol. 80, no. 2-3, pp. 165–188, 2010
2010
Cited alongside, same era.
C.-K. Chiang, T. Yang, C.-J. Lee, M. Mahdavi, C.-J. Lu, R. Jin, and S. Zhu, “Online optimization with gradual variations,” in Conference on Learning Theory , 2012
2012
Later among the works it cites.
2013
Later among the works it cites.
A. Rakhlin and K. Sridharan, “Online learning with predictable sequences,” in Conference on Learning Theory , 2013, pp. 993–1019
2013
Later among the works it cites.
——, “Optimization, learning, and games with predictable sequences,” in Advances in Neural Information Processing Systems , 2013, pp. 3066–3074
2013
Later among the works it cites.
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2012
Cited alongside, same era.
N. Buchbinder, S. Chen, J. Naor, and O. Shamir, “Unified algorithms for online learning and competitive analysis.” Journal of Machine Learning Research-Proceedings Track , vol. 23, pp. 5–1, 2012
2012
Cited alongside, same era.
2013
Later among the works it cites.
C. Daskalakis, A. Deckelbaum, and A. Kim, “Near-optimal no-regret algorithms for zero-sum games,” Games and Economic Behavior , 2014
2014
Later among the works it cites.