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Predictive uncertainty-a model's self awareness regarding its accuracy on an input-is key for both building robust models via training interventions and for test-time applications such as selective classification.
Verification of forecasts expressed in terms of probability
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John Denker, Daniel Schwartz, Ben Wittner, Sara Solla, Richard Howard, Lawrence Jackel, and John Hopfield · 1987
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Bayesian backpropagation
Wray L Buntine · 1991
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul Buenau, and Motoaki Kawanabe · 2007
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Direct importance estimation for covariate shift adaptation
Masashi Sugiyama, Taiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul Von Bünau, and Motoaki Kawanabe · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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On the foundations of noise-free selective classification
Ran El-Yaniv et al · 2010
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Analysis of kernel mean matching under covariate shift
Yao-Liang Yu and Csaba Szepesvári · 2012
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Predicting failures of vision systems
Peng Zhang, Jiuling Wang, Ali Farhadi, Martial Hebert, and Devi Parikh · 2014
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Kaggle. diabetic retinopathy detection challenge, 2015, 2015
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 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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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Uncertainty decomposition in bayesian neural networks with latent variables
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 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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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2018
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Trainable calibration measures for neural networks from kernel mean embeddings
Kumar et al · 2018
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Efficient stochastic gradient descent for learning with distributionally robust optimization
Soumyadip Ghosh, Mark Squillante, and Ebisa Wollega · 2018
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Uncertainty quantification using bayesian neural networks in classification: Application to ischemic stroke lesion segmentation
Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, and Myunghee Cho Paik · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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Inhibited softmax for uncertainty estimation in neural networks
Marcin Możejko, Mateusz Susik, and Rafał Karczewski · 2018
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Uncertainty in neural networks: Approximately bayesian ensembling
Tim Pearce, Felix Leibfried, and Alexandra Brintrup · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 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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Part-dependent label noise: Towards instance-dependent label noise
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, and Masashi Sugiyama · 2020
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Selective classification via one-sided prediction
Aditya Gangrade, Anil Kag, and Venkatesh Saligrama · 2021
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Learning sample reweighting for adversarial robustness
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2018
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Understanding measures of uncertainty for adversarial example detection
Lewis Smith and Yarin Gal · 2018
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A systematic comparison of bayesian deep learning robustness in diabetic retinopathy tasks
Angelos Filos, Sebastian Farquhar, Aidan N Gomez, Tim GJ Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud De Kroon, and Yarin Gal · 2019
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Selectivenet: A deep neural network with an integrated reject option
Yonatan Geifman and Ran El-Yaniv · 2019
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Deep gamblers: Learning to abstain with portfolio theory
Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Chester Holtz, Tsui-Wei Weng, and Gal Mishne · 2021
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 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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Probabilistic margins for instance reweighting in adversarial training
Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama, et al · 2021
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Robust bayesian neural networks by spectral expectation bound regularization
Jiaru Zhang, Yang Hua, Zhengui Xue, Tao Song, Chengyu Zheng, Ruhui Ma, and Haibing Guan · 2021
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Learning fast sample re-weighting without reward data
Zizhao Zhang and Tomas Pfister · 2021
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Efficient and modular implicit differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, and Jean-Philippe Vert · 2022
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Stop overcomplicating selective classification: Use max-logit
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Calibrated selective classification
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Adafocal: Calibration-aware adaptive focal loss
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Selective classification via neural network training dynamics
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Plex: Towards reliability using pretrained large model extensions
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A deeper look into aleatoric and epistemic uncertainty disentanglement
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Model agnostic sample reweighting for out-of-distribution learning
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A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
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Near optimal heteroscedastic regression with symbiotic learning
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Max-margin inspired per-sample reweighitng for robust learning
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