Online Learning and Online Convex Optimization
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[1] I. J. Schoenberg,et al. The Relaxation Method for Linear Inequalities , 1954, Canadian Journal of Mathematics.
[2] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[3] Thomas M. Cover,et al. Behavior of sequential predictors of binary sequences , 1965 .
[4] Marvin Minsky,et al. Perceptrons: An Introduction to Computational Geometry , 1969 .
[5] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[6] Nick Littlestone,et al. From on-line to batch learning , 1989, COLT '89.
[7] Vladimir Vovk,et al. Aggregating strategies , 1990, COLT '90.
[8] N. Littlestone. Mistake bounds and logarithmic linear-threshold learning algorithms , 1990 .
[9] Manfred K. Warmuth,et al. The Weighted Majority Algorithm , 1994, Inf. Comput..
[10] Manfred K. Warmuth,et al. Additive versus exponentiated gradient updates for linear prediction , 1995, STOC '95.
[11] Manfred K. Warmuth,et al. On Weak Learning , 1995, J. Comput. Syst. Sci..
[12] Avrim Blum,et al. On-line Algorithms in Machine Learning , 1996, Online Algorithms.
[13] Dale Schuurmans,et al. General Convergence Results for Linear Discriminant Updates , 1997, COLT '97.
[14] Manfred K. Warmuth,et al. Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..
[15] Yoav Freund,et al. Large Margin Classification Using the Perceptron Algorithm , 1998, COLT' 98.
[16] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[17] Claudio Gentile,et al. The Robustness of the p-Norm Algorithms , 1999, COLT '99.
[18] Geoffrey J. Gordon. Regret bounds for prediction problems , 1999, COLT '99.
[19] Adrian S. Lewis,et al. Convex Analysis And Nonlinear Optimization , 2000 .
[20] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[21] Peter Auer,et al. The Nonstochastic Multiarmed Bandit Problem , 2002, SIAM J. Comput..
[22] C. Zălinescu. Convex analysis in general vector spaces , 2002 .
[23] Dustin Boswell,et al. Introduction to Support Vector Machines , 2002 .
[24] James C. Spall,et al. Introduction to stochastic search and optimization - estimation, simulation, and control , 2003, Wiley-Interscience series in discrete mathematics and optimization.
[25] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[26] Santosh S. Vempala,et al. Efficient algorithms for online decision problems , 2005, J. Comput. Syst. Sci..
[27] Martin Zinkevich,et al. Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.
[28] Peter Auer,et al. Tracking the Best Disjunction , 1998, Machine Learning.
[29] Manfred K. Warmuth,et al. Relative Loss Bounds for Multidimensional Regression Problems , 1997, Machine Learning.
[30] Tim Hesterberg,et al. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control , 2004, Technometrics.
[31] Manfred K. Warmuth,et al. Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions , 1999, Machine Learning.
[32] Claudio Gentile,et al. On the generalization ability of on-line learning algorithms , 2001, IEEE Transactions on Information Theory.
[33] Adam Tauman Kalai,et al. Online convex optimization in the bandit setting: gradient descent without a gradient , 2004, SODA '05.
[34] Tong Zhang. Data Dependent Concentration Bounds for Sequential Prediction Algorithms , 2005, COLT.
[35] Gábor Lugosi,et al. Prediction, learning, and games , 2006 .
[36] Yoram Singer,et al. A primal-dual perspective of online learning algorithms , 2007, Machine Learning.
[37] James C. Spall,et al. Introduction to Stochastic Search and Optimization. Estimation, Simulation, and Control (Spall, J.C. , 2007 .
[38] H. Robbins. Some aspects of the sequential design of experiments , 1952 .
[39] Thomas P. Hayes,et al. The Price of Bandit Information for Online Optimization , 2007, NIPS.
[40] Shai Shalev-Shwartz,et al. Online learning: theory, algorithms and applications (למידה מקוונת.) , 2007 .
[41] Ambuj Tewari,et al. Optimal Stragies and Minimax Lower Bounds for Online Convex Games , 2008, COLT.
[42] Claudio Gentile,et al. Improved Risk Tail Bounds for On-Line Algorithms , 2005, IEEE Transactions on Information Theory.
[43] Elad Hazan,et al. Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization , 2008, COLT.
[44] Shai Ben-David,et al. Agnostic Online Learning , 2009, COLT.
[45] Alexander Rakhlin,et al. Lecture Notes on Online Learning DRAFT , 2009 .
[46] Ambuj Tewari,et al. Online Learning: Random Averages, Combinatorial Parameters, and Learnability , 2010, NIPS.
[47] Elad Hazan. The convex optimization approach to regret minimization , 2011 .
[48] Ambuj Tewari,et al. Regularization Techniques for Learning with Matrices , 2009, J. Mach. Learn. Res..