Fetching the paper…
Reading the bibliography…
Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
Earlier work this paper cites.
Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002
Earlier work this paper cites.
Algorithmic learning in a random world , volume 29
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2005
Earlier work this paper cites.
Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives · 2007
Earlier work this paper cites.
A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
Earlier work this paper cites.
Uncertainty quantification: theory, implementation, and applications , volume 12
Ralph C Smith · 2013
Earlier work this paper cites.
Conformal prediction for reliable machine learning: theory, adaptations and applications
Vineeth Balasubramanian, Shen-Shyang Ho, and Vladimir Vovk · 2014
Earlier work this paper cites.
Classification with confidence
Jing Lei · 2014
Earlier work this paper cites.
Distribution-free prediction bands for non-parametric regression
Jing Lei and Larry Wasserman · 2014
Earlier work this paper cites.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 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.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Charles Lu, Syed Rakin Ahmed, Praveer Singh, and Jayashree Kalpathy-Cramer · 2022
Later among the works it cites.
Learning optimal conformal classifiers
David Stutz, Krishnamurthy Dvijotham, Ali Taylan Cemgil, and Arnaud Doucet · 2022
Later among the works it cites.
Mitigating neural network overconfidence with logit normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li · 2022
Later among the works it cites.
Testing for outliers with conformal p-values
Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano, and Matteo Sesia · 2023
Later among the works it cites.
Improving uncertainty quantification of deep classifiers via neighborhood conformal prediction: Novel algorithm and theoretical analysis
Subhankar Ghosh, Taha Belkhouja, Yan Yan, and Janardhan Rao Doppa · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel Candes · 2019
Cited alongside, same era.
Least ambiguous set-valued classifiers with bounded error levels
Mauricio Sadinle, Jing Lei, and Larry Wasserman · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Training conformal predictors
Nicolo Colombo and Vladimir Vovk · 2020
Cited alongside, same era.
Calibrating deep neural networks using focal loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, and Puneet Dokania · 2020
Cited alongside, same era.
Isaac Gibbs, John J Cherian, and Emmanuel J Candès · 2023
Later among the works it cites.
Conformal prediction with large language models for multi-choice question answering
Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David R. Bellamy, Ramesh Raskar, and Andrew Beam · 2023
Later among the works it cites.
Federated conformal predictors for distributed uncertainty quantification
Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael Jordan, and Ramesh Raskar · 2023
Later among the works it cites.
Improving adaptive conformal prediction using self-supervised learning
Nabeel Seedat, Alan Jeffares, Fergus Imrie, and Mihaela van der Schaar · 2023
Later among the works it cites.
Improving expert predictions with conformal prediction
Eleni Straitouri, Lequn Wang, Nastaran Okati, and Manuel Gomez Rodriguez · 2023
Later among the works it cites.
Calibration in deep learning: A survey of the state-of-the-art
Cheng Wang · 2023
Later among the works it cites.
Beyond confidence: Reliable models should also consider atypicality
Mert Yuksekgonul, Linjun Zhang, James Zou, and Carlos Guestrin · 2023
Later among the works it cites.
Safe merging in mixed traffic with confidence
Heeseung Bang, Aditya Dave, and Andreas A Malikopoulos · 2024
Closest in time.
An information theoretic perspective on conformal prediction
Alvaro HC Correia, Fabio Valerio Massoli, Christos Louizos, and Arash Behboodi · 2024
Closest in time.
Conformal prediction sets improve human decision making
Jesse C Cresswell, Yi Sui, Bhargava Kumar, and Noël Vouitsis · 2024
Closest in time.
On calibration and conformal prediction of deep classifiers
Lahav Dabah and Tom Tirer · 2024
Closest in time.
Conformal prediction for deep classifier via label ranking
Jianguo Huang, HuaJun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, and Hongxin Wei · 2024
Closest in time.
Spatial-aware conformal prediction for trustworthy hyperspectral image classification
Kangdao Liu, Tianhao Sun, Hao Zeng, Yongshan Zhang, Chi-Man Pun, and Chi-Man Vong · 2024
Closest in time.
Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, and Guang Lin · 2024
Closest in time.
Open-vocabulary calibration for fine-tuned CLIP
Shuoyuan Wang, Jindong Wang, Guoqing Wang, Bob Zhang, Kaiyang Zhou, and Hongxin Wei · 2024
Closest in time.