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Hypothesis-pruning maximizes the hypothesis updates for active learning to find those desired unlabeled data.
Elementary applied statistics: for students in behavioral science
Linton C Freeman · 1965
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Comparing robust properties of a, d, e and g-optimal designs
Weng Kee Wong · 1994
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On the complexity of teaching
Sally A Goldman and Michael J Kearns · 1995
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Pac learning from positive statistical queries
François Denis · 1998
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Toward optimal active learning through monte carlo estimation of error reduction
Nicholas Roy and Andrew McCallum · 2001
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A mathematical theory of communication
Claude Elwood Shannon · 2001
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2001
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Active learning in the drug discovery process
Manfred KK Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, and Christian Lemmen · 2001
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Analysis of a greedy active learning strategy
Sanjoy Dasgupta · 2004
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Selective rademacher penalization and reduced error pruning of decision trees
Matti Kääriäinen, Tuomo Malinen, and Tapio Elomaa · 2004
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Query by committee made real
Ran Gilad-Bachrach, Amir Navot, and Naftali Tishby · 2006
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Agnostic active learning
Maria-Florina Balcan, Alina Beygelzimer, and John Langford · 2009
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Importance weighted active learning
Alina Beygelzimer, Sanjoy Dasgupta, and John Langford · 2009
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Adaptive regularization of weight vectors
Koby Crammer, Alex Kulesza, and Mark Dredze · 2009
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Theoretical foundations of active learning
Steve Hanneke · 2009
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Active learning literature survey
Burr Settles · 2009
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Adaptive submodularity: A new approach to active learning and stochastic optimization
Daniel Golovin and Andreas Krause · 2010
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Near-optimal bayesian active learning with noisy observations
Daniel Golovin, Andreas Krause, and Debajyoti Ray · 2010
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Two faces of active learning
Sanjoy Dasgupta · 2011
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Active learning using smooth relative regret approximations with applications
Nir Ailon, Ron Begleiter, and Esther Ezra · 2012
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Activized learning: Transforming passive to active with improved label complexity
Steve Hanneke · 2012
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Efficient active learning of halfspaces: an aggressive approach
Alon Gonen, Sivan Sabato, and Shai Shalev-Shwartz · 2013
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Recursive teaching dimension, vc-dimension and sample compression
Thorsten Doliwa, Gaojian Fan, Hans Ulrich Simon, and Sandra Zilles · 2014
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Towards black-box iterative machine teaching
Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M Rehg, and Le Song · 2018
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Adversarial teacher-student learning for unsupervised domain adaptation
Zhong Meng, Jinyu Li, Yifan Gong, and Biing-Hwang Juang · 2018
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An overview of machine teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles, and Anna N Rafferty · 2018
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Teaching a black-box learner
Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, and Xiaojin Zhu · 2019
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal · 2019
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Teacher–student curriculum learning
Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman · 2019
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Theory of disagreement-based active learning
Steve Hanneke et al · 2014
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Beyond disagreement-based agnostic active learning
Chicheng Zhang and Kamalika Chaudhuri · 2014
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks
Michael Kampffmeyer, Arnt-Borre Salberg, and Robert Jenssen · 2016
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The teaching dimension of linear learners
Ji Liu, Xiaojin Zhu, and Hrag Ohannessian · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Domain adaptation via teacher-student learning for end-to-end speech recognition
Zhong Meng, Jinyu Li, Yashesh Gaur, and Yifan Gong · 2019
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Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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Bayesian batch active learning as sparse subset approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick, and José Miguel Hernández-Lobato · 2019
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Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
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Progressive teacher-student learning for early action prediction
Xionghui Wang, Jian-Fang Hu, Jianhuang Lai, Jianguo Zhang, and Wei-Shi Zheng · 2019
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Shattering distribution for active learning
Xiaofeng Cao and Ivor W Tsang · 2020
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Teaching with limited information on the learner’s behaviour
Ferdinando Cicalese, Eduardo Laber, Marco Molinaro, et al · 2020
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Adaptive region-based active learning
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, and Ningshan Zhang · 2020
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Consistency-based semi-supervised active learning: Towards minimizing labeling cost
Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ö Arık, Larry S Davis, and Tomas Pfister · 2020
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Viewal: Active learning with viewpoint entropy for semantic segmentation
Yawar Siddiqui, Julien Valentin, and Matthias Nießner · 2020
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