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Uncertainty quantification is essential for the reliable deployment of machine learning models to high-stakes application domains.
Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 1912
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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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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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Algorithmic learning in a random world
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
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Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman · 2007
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Dataset shift in machine learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 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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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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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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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Concrete dropout
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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Adversarial multiple source domain adaptation
Han Zhao, Shanghang Zhang, Guanhang Wu, José MF Moura, Joao P Costeira, and Geoffrey J Gordon · 2018
Calibrated prediction with covariate shift via unsupervised domain adaptation
Sangdon Park, Osbert Bastani, James Weimer, and Insup Lee · 2020
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Transferable calibration with lower bias and variance in domain adaptation
Ximei Wang, Mingsheng Long, Jianmin Wang, and Michael Jordan · 2020
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Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval
Tobias Weyand, Andre Araujo, Bingyi Cao, and Jack Sim · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L Yuille, and Quoc V Le · 2020
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Learn then test: Calibrating predictive algorithms to achieve risk control
Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès, Michael I. Jordan, and Lihua Lei · 2021
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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Conformal prediction under covariate shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya Ramdas · 2019
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Robust validation: Confident predictions even when distributions shift
Maxime Cauchois, Suyash Gupta, Alnur Ali, and John C Duchi · 2020
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Distribution-free, risk-controlling prediction sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael I. Jordan · 2021
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Predicting with confidence on unseen distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, and Ludwig Schmidt · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021
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Pac prediction sets under covariate shift
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani · 2021
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Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev and Aaditya Ramdas · 2021
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On calibration and out-of-domain generalization
Yoav Wald, Amir Feder, Daniel Greenfeld, and Uri Shalit · 2021
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T-cal: An optimal test for the calibration of predictive models
Donghwan Lee, Xinmeng Huang, Hamed Hassani, and Edgar Dobriban · 2022
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