Fetching the paper…
Reading the bibliography…
Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
J. Platt · 1999
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
B. Zadrozny and C. Elkan · 2001
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
B. Zadrozny and C. Elkan · 2002
Earlier work this paper cites.
Using confidence intervals for graphically based data interpretation
M. E. J. Masson and G. R. Loftus · 2003
Earlier work this paper cites.
Calibrating predictive model estimates to support personalized medicine
Xiaoqian Jiang, M. Osl, J. Kim, and L. Ohno-Machado · 2012
Earlier work this paper cites.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
R. Caruana, Yin Lou, J. Gehrke, Paul Koch, M. Sturm, and Noémie Elhadad · 2015
Earlier work this paper cites.
Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using Bayesian binning
M. Naeini, G. Cooper, and M. Hauskrecht · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, D. Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, L. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, X. Zhang, Jake Zhao, and Karol Zieba · 2016
Earlier work this paper cites.
Binary classifier calibration using an ensemble of near isotonic regression models
M. Naeini and G. Cooper · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Estimating uncertainty online against an adversary
Volodymyr Kuleshov and S. Ermon · 2017
Earlier work this paper cites.
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Meelis Kull, Telmo de Menezes e Silva Filho, and Peter A. Flach · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Cited alongside, same era.
On fairness and calibration, 2017
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis · 2017
Cited alongside, same era.
Learning confidence for out-of-distribution detection in neural networks
Terrance Devries and Graham W. Taylor · 2018
Cited alongside, same era.
Trainable calibration measures for neural networks from kernel mean embeddings
A. Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
Cited alongside, same era.
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
Later among the works it cites.
Calibration of neural networks using splines
Kartik Gupta, Amir M. Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, C. Sminchisescu, and R. Hartley · 2020
Later among the works it cites.
Simon Kocbek, Primoz Kocbek, Leona Cilar, and Gregor Stiglic · 2020
Later among the works it cites.
Improving model calibration with accuracy versus uncertainty optimization
R. Krishnan and O. Tickoo · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2018
Cited alongside, same era.
Non-parametric Bayesian isotonic calibration: Fighting over-confidence in binary classification
Mari-Liis Allikivi and Meelis Kull · 2019
Cited alongside, same era.
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, and Peter A. Flach · 2019
Cited alongside, same era.
Verified uncertainty calibration
Ananya Kumar, Percy Liang, and Tengyu Ma · 2019
Cited alongside, same era.
Measuring calibration in deep learning
Jeremy Nixon, Michael W Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, E. Fertig, J. Ren, Zachary Nado, D. Sculley, S. Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Cited alongside, same era.
Robustness Metrics, 2020
Josip Djolonga, Minderer Matthias, Zack Nado, Jeremy Nixon, Rob Romijnders, Dustin Tran, and Mario Lucic · 2020
Cited alongside, same era.
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, S. Golodetz, P. Torr, and P. Dokania · 2020
Later among the works it cites.
When does label smoothing help?, 2020
Rafael Müller, Simon Kornblith, and Geoffrey Hinton · 2020
Later among the works it cites.
Mitigating bias in calibration error estimation
R. Roelofs, N. Cain, Jonathon Shlens, and M. Mozer · 2020
Later among the works it cites.
Calibrating deep neural network classifiers on out-of-distribution datasets
Zhihui Shao, Jianyi Yang, and Shaolei Ren · 2020
Later among the works it cites.
BatchEnsemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
Later among the works it cites.
Non-parametric calibration for classification
Jonathan Wenger, H. Kjellström, and Rudolph Triebel · 2020
Later among the works it cites.
Evaluation of neural architectures trained with square loss vs cross-entropy in classification tasks, 2021
Like Hui and Mikhail Belkin · 2021
Closest in time.
Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning, 2021
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, and Dustin Tran · 2021
Closest in time.