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Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points.
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R. M. Dudley · 1987
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A. W. van der Vaart and J. A. Wellner · 1996
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A decision-theoretic generalization of on-line learning and an application to boosting
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Efficient noise-tolerant learning from statistical queries
M. J. Kearns · 1998
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Smooth discrimination analysis
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Rademacher penalties and structural risk minimization
V. Koltchinskii · 2001
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Rademacher and Gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
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On the rate of convergence of regularized boosting classifiers
G. Blanchard, G. Lugosi, and N. Vayatis · 2003
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Ratio limit theorems for empirical processes
E. Giné, V. Koltchinskii, and J. Wellner · 2003
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Optimal aggregation of classifiers in statistical learning
A. B. Tsybakov · 2004
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Statistical behavior and consistency of classification methods based on convex risk minimization
T. Zhang · 2004
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Local Rademacher complexities
P. L. Bartlett, O. Bousquet, and S. Mendelson · 2005
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Agnostically learning halfspaces
A. T. Kalai, A. R. Klivans, Y. Mansour, and R. A. Servedio · 2005
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Convexity, classification, and risk bounds
P. Bartlett, M. I. Jordan, and J. McAuliffe · 2006
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Introduction to Nonparametric Estimation
A. B. Tsybakov · 2009
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The true sample complexity of active learning
M.-F. Balcan, S. Hanneke, and J. W. Vaughan · 2010
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Negative results for active learning with convex losses
S. Hanneke and L. Yang · 2010
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Rademacher complexities and bounding the excess risk in active learning
V. Koltchinskii · 2010
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Learning noisy linear classifiers via adaptive and selective sampling
G. Cavallanti, N. Cesa-Bianchi, and C. Gentile · 2011
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Rates of convergence in active learning
S. Hanneke · 2011
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Concentration inequalities and asymptotic results for ratio type empirical processes
E. Giné and V. Koltchinskii · 2006
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Local Rademacher complexities and oracle inequalities in risk minimization
V. Koltchinskii · 2006
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Fast learning rates for plug-in classifiers
J.-Y. Audibert and A. B. Tsybakov · 2007
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A general agnostic active learning algorithm
S. Dasgupta, D. Hsu, and C. Monteleoni · 2007
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A bound on the label complexity of agnostic active learning
S. Hanneke · 2007
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Minimax bounds for active learning
R. Castro and R. Nowak · 2008
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Concise formulas for the area and volume of a hyperspherical cap
S. Li · 2011
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A note on active learning for smooth problems
S. Mahalanabis · 2011
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Lower bounds for passive and active learning
M. Raginsky and A. Rakhlin · 2011
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A local maximal inequality under uniform entropy
A. W. van der Vaart and J. A. Wellner · 2011
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Smoothness, disagreement coefficient, and the label complexity of agnostic active learning
L. Wang · 2011
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Selective sampling and active learning from single and multiple teachers
O. Dekel, C. Gentile, and K. Sridharan · 2012
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Activized learning: Transforming passive to active with improved label complexity
S. Hanneke · 2012
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Plug-in approach to active learning
S. Minsker · 2012
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Theory of disagreement-based active learning
S. Hanneke · 2014
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Nonparametric active learning, part 1: Smooth regression functions
S. Hanneke · 2016
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Adaptivity to noise parameters in nonparametric active learning
A. Locatelli, A. Carpentier, and S. Kpotufe · 2017
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