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As machine learning (ML) is deployed by many competing service providers, the underlying ML predictors also compete against each other, and it is increasingly important to understand the impacts and biases from such competition.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
Earlier work this paper cites.
Selective sampling using the query by committee algorithm
Yoav Freund, H Sebastian Seung, Eli Shamir, and Naftali Tishby · 1997
Earlier work this paper cites.
Promotion and marketing communications in the information marketplace
Jennifer Rowley · 1998
Earlier work this paper cites.
Coil challenge 2000: The insurance company case
Peter Van Der Putten and Maarten van Someren · 2000
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Pool-based active learning with optimal sampling distribution and its information geometrical interpretation
Takafumi Kanamori · 2007
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Active learning literature survey
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Earlier work this paper cites.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
Libsvm: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
Earlier work this paper cites.
Rates of convergence in active learning
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Earlier work this paper cites.
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Adam: A method for stochastic optimization
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Data products
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