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The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions.
An analysis of approximations for maximizing submodular set functions—i
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David Cohn, Les Atlas, and Richard Ladner · 1994
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Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Leonard Kaufman and Peter J Rousseeuw · 2009
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Graph-based submodular selection for extractive summarization
Hui Lin, Jeff Bilmes, and Shasha Xie · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Andreas Maurer and Massimiliano Pontil · 2009
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Hae-Sang Park and Chi-Hyuck Jun · 2009
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Ryan Gomes and Andreas Krause · 2010
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Off to a good start: Using clustering to select the initial training set in active learning
Rong Hu, Brian Mac Namee, and Sarah Jane Delany · 2010
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An automatic method for solving discrete programming problems
Ailsa H Land and Alison G Doig · 2010
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Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Zalán Bodó, Zsolt Minier, and Lehel Csató · 2011
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Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Deep Bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Global optimization of lipschitz functions
Cédric Malherbe and Nicolas Vayatis · 2017
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Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto · 2017
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A sub-quadratic exact medoid algorithm
James Newling and François Fleuret · 2017
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Active multi-kernel domain adaptation for hyperspectral image classification
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Active supervised domain adaptation
Avishek Saha, Piyush Rai, Hal Daumé, Suresh Venkatasubramanian, and Scott L DuVall · 2011
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Towards active learning on graphs: An error bound minimization approach
Q. Gu and J. Han · 2012
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Submodular optimization with submodular cover and submodular knapsack constraints
Rishabh K Iyer and Jeff A Bilmes · 2013
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Deep batch active learning by diverse, uncertain gradient lower bounds
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Adapt: Awesome domain adaptation python toolbox
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