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Modern machine learning models with high accuracy are often miscalibrated -- the predicted top probability does not reflect the actual accuracy, and tends to be over-confident.
On the existence of maximum likelihood estimates in logistic regression models
Adelin Albert and John A Anderson · 1984
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
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Asymptotic statistics , volume 3
Aad W Van der Vaart · 2000
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Message-passing algorithms for compressed sensing
David L Donoho, Arian Maleki, and Andrea Montanari · 2009
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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The dynamics of message passing on dense graphs, with applications to compressed sensing
Mohsen Bayati and Andrea Montanari · 2011
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Efficient learning of generalized linear and single index models with isotonic regression
Sham Kakade, Adam Tauman Kalai, Varun Kanade, and Ohad Shamir · 2011
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On robust regression with high-dimensional predictors
Noureddine El Karoui, Derek Bean, Peter J Bickel, Chinghway Lim, and Bin Yu · 2013
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Noureddine El Karoui · 2013
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A framework to characterize performance of lasso algorithms
Mihailo Stojnic · 2013
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Regularized linear regression: A precise analysis of the estimation error
Christos Thrampoulidis, Samet Oymak, and Babak Hassibi · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Yarin Gal, Jiri Hron, and Alex Kendall · 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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Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Meelis Kull, Telmo Silva Filho, and Peter Flach · 2017
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Generalized linear models
Peter McCullagh · 2018
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Evaluating uncertainty quantification in end-to-end autonomous driving control
Rhiannon Michelmore, Marta Kwiatkowska, and Yarin Gal · 2018
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A modern maximum-likelihood theory for high-dimensional logistic regression
Pragya Sur and Emmanuel J Candès · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression
Emmanuel J Candès, Pragya Sur, et al · 2020
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Local temperature scaling for probability calibration
Zhipeng Ding, Xu Han, Peirong Liu, and Marc Niethammer · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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Precise error analysis of regularized m m -estimators in high dimensions
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2018
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The need for uncertainty quantification in machine-assisted medical decision making
Edmon Begoli, Tanmoy Bhattacharya, and Dimitri Kusnezov · 2019
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Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Hao Song, Peter Flach, et al · 2019
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Verified uncertainty calibration
Ananya Kumar, Percy Liang, and Tengyu Ma · 2019
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The implicit fairness criterion of unconstrained learning
Lydia T Liu, Max Simchowitz, and Moritz Hardt · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 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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Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh M Pai, Aaron Roth, and Rakesh Vohra · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan · 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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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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Sample complexity of uniform convergence for multicalibration
Eliran Shabat, Lee Cohen, and Yishay Mansour · 2020
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Fundamental limits of ridge-regularized empirical risk minimization in high dimensions
Hossein Taheri, Ramtin Pedarsani, and Christos Thrampoulidis · 2020
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Hydra: Preserving ensemble diversity for model distillation
Linh Tran, Bastiaan S Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, and Rodolphe Jenatton · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Jize Zhang, Bhavya Kailkhura, and T Yong-Jin Han · 2020
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