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In safety-critical applications of machine learning, it is often important to abstain from making predictions on low confidence examples.
The nearest neighbor classification rule with a reject option
Martin E Hellman · 1970
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On the psychology of prediction
Daniel Kahneman and Amos Tversky · 1973
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The meaning and use of the area under a receiver operating characteristic (ROC) curve
J A Hanley and B J McNeil · 1982
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Neural network classification: a bayesian interpretation
E A Wan · 1990
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Safety-critical systems, formal methods and standards
Jonathan Bowen and Victoria Stavridou · 1993
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A method for improving classification reliability of multilayer perceptrons
L P Cordella, C De Stefano, F Tortorella, and M Vento · 1995
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On the reality of cognitive illusions
Daniel Kahneman and Amos Tversky · 1996
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
J. C. Platt · 1999
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To reject or not to reject: that is the question-an answer in case of neural classifiers
Claudio De Stefano, Carlo Sansone, and Mario Vento · 2000
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Reject option with multiple thresholds
Giorgio Fumera, Fabio Roli, and Giorgio Giacinto · 2000
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Support vector machines with embedded reject option
Giorgio Fumera and Fabio Roli · 2002
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Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
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Optimizing abstaining classifiers using ROC analysis
Tadeusz Pietraszek · 2005
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Asirra: A captcha that exploits interest-aligned manual image categorization
J. Elson, J. R. Douceur, J. Howell, and J. Saul · 2007
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Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller · 2007
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Classification with a reject option using a hinge loss
Peter L Bartlett and Marten H Wegkamp · 2008
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When training and test sets are different: characterizing learning transfer
Amos Storkey · 2009
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On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
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Cognitive models of test-item effects in human category learning
Xiaojin Zhu, Bryan R Gibson, Kwang-Sung Jun, Timothy T Rogers, Joseph Harrison, and Chuck Kalish · 2010
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Learning word vectors for sentiment analysis
Selective Classification for Deep Neural Networks
Y. Geifman and R. El-Yaniv · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Leveraging uncertainty information from deep neural networks for disease detection
Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan, Philipp Berens, and Siegfried Wahl · 2017
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https://blog.quantopian.com/bayesian-deep-learning/
Bayesian deep learning - quantopian blog · 2018
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Know when you don’t know: A robust deep learning approach in the presence of unknown phenotypes
Oliver Dürr, Elvis Murina, Daniel Siegismund, Vasily Tolkachev, Stephan Steigele, and Beate Sick · 2018
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A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts · 2011
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Calibrating predictive model estimates to support personalized medicine
X. Jiang, M. Osl, J. Kim, and L. Ohno-Machado · 2012
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On causal and anticausal learning
Bernhard Schoelkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Bayesian convolutional neural networks with bernoulli approximate variational inference
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Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution
M. R. Corces, J. D. Buenrostro, B. Wu, P. G. Greenside, S. M. Chan, J. L. Koenig, M. P. Snyder, J. K. Pritchard, A. Kundaje, W. J. Greenleaf, R. Majeti, and H. Y. Chang · 2016
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Boosting with abstention
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
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Bayesian Hypernetworks
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Detecting and correcting for label shift with black box predictors
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Learning under concept drift: A review
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Estimating uncertainty in mrf-based image segmentation: A perfect-mcmc approach
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Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation
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Selective classification can magnify disparities across groups
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