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This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set.
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“One-class classifier networks for target recognition applications”
Mary Moya, Mark Koch and Larry Hostetler · 1993
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“Controlling the false discovery rate: a practical and powerful approach to multiple testing”
Yoav Benjamini and Yosef Hochberg · 1995
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“Multiple Hypotheses Testing with Weights”
Yoav Benjamini and Yosef Hochberg · 1997
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“Machine-learning applications of algorithmic randomness”
Vladimir Vovk, Alexander Gammerman and Craig Saunders · 1999
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“The control of the false discovery rate in multiple testing under dependency”
Yoav Benjamini and Daniel Yekutieli · 2001
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“A direct approach to false discovery rates”
John Storey · 2002
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“Novelty detection: a review—part 1: statistical approaches”
Markos Markou and Sameer Singh · 2003
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“Mondrian Confidence Machine” On-line Compression Modelling project, On-line Compression Modelling project, 2003
Vladimir Vovk, David Lindsay, Ilia Nouretdinov and Alex Gammerman · 2003
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“Outlier detection using k-nearest neighbour graph”
Ville Hautamaki, Ismo Karkkainen and Pasi Franti · 2004
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“Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach”
John Storey, Jonathan Taylor and David Siegmund · 2004
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“Algorithmic learning in a random world”
Vladimir Vovk, Alex Gammerman and Glenn Shafer · 2005
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“False discovery control with p-value weighting”
Christopher Genovese, Kathryn Roeder and Larry Wasserman · 2006
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“False Discovery Control with p-Value Weighting”
Christopher. Genovese, Kathryn Roeder and Larry Wasserman · 2006
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“Optimal weighting for false discovery rate control”
Etienne Roquain and Mark Van · 2008
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“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“False discovery rate control with groups”
James Hu, Hongyu Zhao and Harrison Zhou · 2010
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“Neyman-pearson classification, convexity and stochastic constraints”
Philippe Rigollet and Xin Tong · 2011
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“Scikit-learn: Machine Learning in Python”
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and E. Duchesnay · 2011
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“Large-scale inference: empirical Bayes methods for estimation, testing, and prediction”
Bradley Efron · 2012
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“Conditional validity of inductive conformal predictors”
Vladimir Vovk · 2012
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“One class random forests”
Chesner Désir, Simon Bernard, Caroline Petitjean and Laurent Heutte · 2013
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“Transductive conformal predictors”
Vladimir Vovk · 2013
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“Effective utilization of data in inductive conformal prediction”
Tuve Löfström, Ulf Johansson and Henrik Boström · 2013
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“A plug-in approach to neyman-pearson classification”
Xin Tong · 2013
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“3d object representations for fine-grained categorization”
Jonathan Krause, Michael Stark, Jia Deng and Li Fei-Fei · 2013
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“OpenML: networked science in machine learning”
Joaquin Vanschoren, Jan. van Rijn, Bernd Bischl and Luis Torgo · 2013
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“A review of novelty detection”
Marco Pimentel, David Clifton, Lei Clifton and Lionel Tarassenko · 2014
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“Probabilistic novelty detection with support vector machines”
Lei Clifton, David Clifton, Yang Zhang, Peter Watkinson, Lionel Tarassenko and Hujun Yin · 2014
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“One-class classification: taxonomy of study and review of techniques”
Shehroz Khan and Michael Madden · 2014
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“Distribution-free prediction bands for non-parametric regression”
Jing Lei and Larry Wasserman · 2014
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“Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories”
Rikard Laxhammar and Göran Falkman · 2015
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“Conformal anomaly detection of trajectories with a multi-class hierarchy”
James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer and Alexander Gammerman · 2015
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“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
Martín, Ashish, Paul, Eugene, Zhifeng, Craig, Greg., Andy, Jeffrey, Matthieu, Sanjay, Ian, Andrew, Geoffrey, Michael, Yangqing Jia, Rafal, Lukasz, Manjunath, Josh, Dandelioné, Rajat, Sherry, Derek, Chris, Mike, Jonathon, Benoit, Ilya, Kunal, Paul, Vincent, Vijay, Fernandaégas, Oriol, Pete, Martin, Martin, Yuan and Xiaoqiang · 2015
Cited alongside, same era.
“Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems”
Feiyang Cai and Xenofon Koutsoukos · 2020
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“Classification with Valid and Adaptive Coverage”
Yaniv Romano, Matteo Sesia and Emmanuel. Candès · 2020
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“Adaptive, distribution-free prediction intervals for deep networks”
Danijel Kivaranovic, Kory Johnson and Hannes Leeb · 2020
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“Flexible distribution-free conditional predictive bands using density estimators”
Rafael Izbicki, Gilson Shimizu and Rafael Stern · 2020
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“Uncertainty sets for image classifiers using conformal prediction”
Anastasios Angelopoulos, Stephen Bates, Jitendra Malik and Michael Jordan · 2020
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“Training conformal predictors”
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“Data-driven hypothesis weighting increases detection power in genome-scale multiple testing”
Nikolaos Ignatiadis, Bernd Klaus, Judith Zaugg and Wolfgang Huber · 2016
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“Neyman-Pearson classification under high-dimensional settings”
Anqi Zhao, Yang Feng, Lie Wang and Xin Tong · 2016
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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Conformal k k -NN Anomaly Detector for Univariate Data Streams”
Vladislav Ishimtsev, Alexander Bernstein, Evgeny Burnaev and Ivan Nazarov · 2017
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“Accumulation tests for FDR control in ordered hypothesis testing”
Ang Li and Rina Barber · 2017
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“UCI Machine Learning Repository”, 2017
Dheeru Dua and Casey Graff · 2017
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“Adversarially learned one-class classifier for novelty detection”
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy and Ehsan Adeli · 2018
Cited alongside, same era.
Nicolo Colombo and Vladimir Vovk · 2020
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“Efficient conformal predictor ensembles”
Henrik Linusson, Ulf Johansson and Henrik Boström · 2020
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“Predictive inference is free with the jackknife+-after-bootstrap”
Byol Kim, Chen Xu and Rina Barber · 2020
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“Conditional calibration for false discovery rate control under dependence”
William Fithian and Lihua Lei · 2020
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“Testing for outliers with conformal p-values”
Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano and Matteo Sesia · 2021
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“A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks”
Matan Haroush, Tzviel Frostig, Ruth Heller and Daniel Soudry · 2021
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“Semi-supervised multiple testing”
David Mary and Etienne Roquain · 2021
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“A gentle introduction to conformal prediction and distribution-free uncertainty quantification”
Anastasios Angelopoulos and Stephen Bates · 2021
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“Covariate powered cross-weighted multiple testing”
Nikolaos Ignatiadis and Wolfgang Huber · 2021
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“LAWS: A Locally Adaptive Weighting and Screening Approach To Spatial Multiple Testing”
T Cai, Wenguang Sun and Yin Xia · 2021
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“Predictive inference with the jackknife+”
Rina Barber, Emmanuel Candès, Aaditya Ramdas and Ryan Tibshirani · 2021
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“Distributional conformal prediction”
Victor Chernozhukov, Kaspar Wüthrich and Yinchu Zhu · 2021
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“Conformal Prediction using Conditional Histograms”
Matteo Sesia and Yaniv Romano · 2021
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“Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction.”
Maxime Cauchois, Suyash Gupta and John Duchi · 2021
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“Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data”
Chen Xu and Yao Xie · 2021
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“A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification”
Bradley Rava, Wenguang Sun, Gareth James and Xin Tong · 2021
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“Locally Adaptive Transfer Learning Algorithms for Large-Scale Multiple Testing”
Ziyi Liang, T Cai, Wenguang Sun and Yin Xia · 2022
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“Training Uncertainty-Aware Classifiers with Conformalized Deep Learning”
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