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A multiclass classifier is said to be top-label calibrated if the reported probability for the predicted class -- the top-label -- is calibrated, conditioned on the top-label.
The equivalence of weak, strong and complete convergence in L 1 {L}_{1} for kernel density estimates
Luc Devroye · 1983
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Almost surely consistent nonparametric regression from recursive partitioning schemes
Louis Gordon and Richard A Olshen · 1984
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Automatic pattern recognition: A study of the probability of error
Luc Devroye · 1988
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Consistency of data-driven histogram methods for density estimation and classification
Gábor Lugosi and Andrew Nobel · 1996
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Histogram regression estimation using data-dependent partitions
Andrew Nobel · 1996
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Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
Jock A Blackard and Denis J Dean · 1999
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John C. Platt · 1999
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Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Inequalities for the L1 deviation of the empirical distribution
Tsachy Weissman, Erik Ordentlich, Gadiel Seroussi, Sergio Verdu, and Marcelo J Weinberger · 2003
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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XSEDE: Accelerating Scientific Discovery
J. Towns, T. Cockerill, M. Dahan, I. Foster, K. Gaither, A. Grimshaw, V. Hazlewood, S. Lathrop, D. Lifka, G. D. Peterson, R. Roskies, J. Scott, and N. Wilkins-Diehr · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Classification and regression trees
Leo Breiman, Jerome H Friedman, Richard A Olshen, and Charles J Stone · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Calibration tests in multi-class classification: a unifying framework
David Widmann, Fredrik Lindsten, and Dave Zachariah · 2019
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Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta, Aleksandr Podkopaev, and Aaditya Ramdas · 2020
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Calibrating deep neural networks using focal loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip HS Torr, and Puneet K Dokania · 2020
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Measuring calibration in deep learning
Jeremy Nixon, Michael W Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2020
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Concentration inequalities for multinoulli random variables
Jian Qian, Ronan Fruit, Matteo Pirotta, and Alessandro Lazaric · 2020
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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Cited alongside, same era.
Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration
Meelis Kull, Telmo M. Silva Filho, and Peter Flach · 2017
Cited alongside, same era.
Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
Cited alongside, same era.
Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
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Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Hao Song, and Peter Flach · 2019
Cited alongside, same era.
Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
Cited alongside, same era.
Intra order-preserving functions for calibration of multi-class neural networks
Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, and Byron Boots · 2020
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Mitigating bias in calibration error estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, and Michael C Mozer · 2020
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Jize Zhang, Bhavya Kailkhura, and T Han · 2020
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Distribution-free calibration guarantees for histogram binning without sample splitting
Chirag Gupta and Aaditya Ramdas · 2021
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Calibration of neural networks using splines
Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, and Richard Hartley · 2021
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Multi-class uncertainty calibration via mutual information maximization-based binning
Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, and Dan Zhang · 2021
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Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev and Aaditya Ramdas · 2021
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