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How do we know when the predictions made by a classifier can be trusted? This is a fundamental problem that also has immense practical applicability, especially in safety-critical areas such as medicine and autonomous driving.
Confident learning: Estimating uncertainty in dataset labels
Curtis G. Northcutt, Lu Jiang, and Isaac L. Chuang · 1911
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Multidimensional b-trees for associative searching in database systems
Peter Scheuermann and Mohamed Ouksel · 1982
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Discriminatory analysis. nonparametric discrimination: Consistency properties
Evelyn Fix and Joseph Lawson Hodges · 1989
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The multilayer perceptron as an approximation to a bayes optimal discriminant function
Dennis W Ruck, Steven K Rogers, Matthew Kabrisky, Mark E Oxley, and Bruce W Suter · 1990
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An introduction to logistic regression analysis and reporting
Chao-Ying Joanne Peng, Kuk Lida Lee, and Gary M Ingersoll · 2002
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Random forest: a classification and regression tool for compound classification and qsar modeling
Vladimir Svetnik, Andy Liaw, Christopher Tong, J Christopher Culberson, Robert P Sheridan, and Bradley P Feuston · 2003
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux · 2013
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Improving memory space efficiency of kd-tree for real-time ray tracing
Byeongjun Choi, Byungjoon Chang, and Insung Ihm · 2013
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Conformal prediction for reliable machine learning: theory, adaptations and applications
Vineeth Balasubramanian, Shen-Shyang Ho, and Vladimir Vovk · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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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Nearest neighbors in high-dimensional spaces
Alexandr Andoni and Piotr Indyk · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 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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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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
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Pruned kd-tree: a memory-efficient algorithm for multi-field packet classification
M Rafiee and M Abbasi · 2019
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Probabilistic face embeddings
Yichun Shi and Anil K Jain · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Data uncertainty learning in face recognition
Jie Chang, Zhonghao Lan, Changmao Cheng, and Yichen Wei · 2020
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Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
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On the safety of machine learning: Cyber-physical systems, decision sciences, and data products
K. Varshney and H. Alemzadeh · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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To trust or not to trust a classifier
Heinrich Jiang, Been Kim, Melody Y Guan, and Maya Gupta · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark John Francis Gales · 2018
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Quora insincere questions classification competition, 2018
Quora and Kaggle · 2018
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Bertrand Charpentier, Daniel Zügner, and Stephan Günnemann · 2020
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Ensemble distribution distillation
Andrey Malinin, Bruno Mlodozeniec, and Mark John Francis Gales · 2020
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Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, and Kilian Q Weinberger · 2020
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Detecting out-of-distribution examples with gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2020
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Distribution-free calibration guarantees for histogram binning without sample splitting
Chirag Gupta and Aaditya K Ramdas · 2021
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Spherical confidence learning for face recognition
Shen Li, Jianqing Xu, Xiaqing Xu, Pengcheng Shen, Shaoxin Li, and Bryan Hooi · 2021
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Be confident! towards trustworthy graph neural networks via confidence calibration
Xiao Wang, Hongrui Liu, Chuan Shi, and Cheng Yang · 2021
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Learning from noisy labels with no change to the training process
Mingyuan Zhang, Jane Lee, and Shivani Agarwal · 2021
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Trust, but verify: Using self-supervised probing to improve trustworthiness
Ailin Deng, Shen Li, Miao Xiong, Zhirui Chen, and Bryan Hooi · 2022
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Poem: Out-of-distribution detection with posterior sampling
Yifei Ming, Ying Fan, and Yixuan Li · 2022
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