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Given $k$ pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries? Answering this question has a profound impact on a range of practical scenarios.
Query by committee
H Sebastian Seung, Manfred Opper, and Haim Sompolinsky · 1992
Earlier work this paper cites.
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David A. Cohn, Les E. Atlas, and Richard E. Ladner · 1994
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The Weighted Majority Algorithm
Nick Littlestone and Manfred K Warmuth · 1994
Earlier work this paper cites.
Committee-based sampling for training probabilistic classifiers
Ido Dagan and Sean P. Engelson · 1995
Earlier work this paper cites.
A desicion-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1995
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
How to use expert advice
Nicolo Cesa-Bianchi, Yoav Freund, David Haussler, David P Helmbold, Robert E Schapire, and Manfred K Warmuth · 1997
Earlier work this paper cites.
Query learning strategies using boosting and bagging
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Earlier work this paper cites.
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Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
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Omid Madani, Daniel J. Lizotte, and Russell Greiner · 2004
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Diverse ensembles for active learning
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Minimizing regret with label efficient prediction
Nicolo Cesa-Bianchi, Gábor Lugosi, and Gilles Stoltz · 2005
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Active learning for misspecified models
Masashi Sugiyama · 2006
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Active learning for misspecified generalized linear models
Francis R. Bach · 2007
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Xingquan Zhu, Peng Zhang, Xiaodong Lin, and Yong Shi · 2007
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Alina Beygelzimer, Sanjoy Dasgupta, and John Langford · 2008
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Sanjoy Dasgupta, Daniel J Hsu, and Claire Monteleoni · 2008
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Maria-Florina Balcan, Alina Beygelzimer, and John Langford · 2009
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Burr Settles · 2009
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Follow the Leader If You Can, Hedge If You Must
Steven de Rooij, Tim van Erven, Peter D. Grünwald, and Wouter M. Koolen · 2013
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Alnur Ali, Rich Caruana, and Ashish Kapoor · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Beyond disagreement-based agnostic active learning
Chicheng Zhang and Kamalika Chaudhuri · 2014
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Bayesian active model selection with an application to automated audiometry
Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q Weinberger, Dennis Barbour, and John P Cunningham · 2015
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Alina Beygelzimer, Daniel J Hsu, John Langford, and Tong Zhang · 2010
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Active risk estimation
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Efficient active learning
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Namit Katariya, Arun Iyer, and Sunita Sarawagi · 2012
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Interactive structure learning with structural query-by-committee
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Introduction to online convex optimization
Elad Hazan · 2019
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Consistent online optimization: Convex and submodular
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URL https://www.humanizing-ai.com/emocontext.html
SemEval, 2019 · 2019
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Introduction to Multi-Armed Bandits
Aleksandrs Slivkins · 2019
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