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Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications.
A theory of the learnable
1984
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Queries and concept learning
1988
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Approximation by superpositions of a sigmoidal function
1989
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Multilayer feedforward networks are universal approximators
1989
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Occam’s razor
1990
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Learnability with respect to fixed distributions
1991
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A technique for upper bounding the spectral norm with applications to learning
1992
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A training algorithm for optimal margin classifiers
1992
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Learning decision trees using the Fourier spectrum
1993
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Extracting refined rules from knowledge-based neural networks
1993
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Improving generalization with active learning
1994
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An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
1994
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Survey and critique of techniques for extracting rules from trained artificial neural networks
1995
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Exact learning boolean functions via the monotone theory
1995
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Active learning literature survey
1995
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Extracting tree-structured representations of trained networks
1996
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Selection of relevant features and examples in machine learning
1997
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Kernel logistic regression and the import vector machine
2001
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Adversarial classification
2004
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Adversarial learning
2005
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Good word attacks on statistical spam filters
2005
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Can machine learning be secure?
2006
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Model compression
2006
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Differential privacy
2006
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Paragraph: Thwarting signature learning by training maliciously
2006
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Numerical optimization
2006
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Evasion attacks against machine learning at test time
2013
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Membership privacy: A unifying framework for privacy definitions
2013
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UCI machine learning repository, 2013
2013
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General social surveys, 1972-2012, 2013
2013
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On the hardness of evading combinations of linear classifiers
2013
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Privacy in pharmacogenetics: An end-to-end case study of personalized Warfarin dosing
2014
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Practical evasion of a learning-based classifier: A case study
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2007
Cited alongside, same era.
Privacy-preserving logistic regression
2009
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A practical differentially private random decision tree classifier
2009
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Antidote: understanding and defending against poisoning of anomaly detectors
2009
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Online anomaly detection under adversarial impact
2010
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2014
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
2015
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Model inversion attacks that exploit confidence information and basic countermeasures
2015
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Distilling the knowledge in a neural network
2015
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Privacy-preserving deep learning
2015
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https://aws.amazon.com/machine-learning
2016
Closest in time.
https://www.bigml.com
2016
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https://cloud.google.com/prediction
2016
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How Americans Like their Steak
2016
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https://azure.microsoft.com/services/machine-learning
2016
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Practical black-box attacks against deep learning systems using adversarial examples
2016
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http://prediction.io
2016
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