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Recently there has been a surge of interest to deploy confidence set predictions rather than point predictions in machine learning.
Conformal prediction: A gentle introduction
Anastasios N. Angelopoulos and Stephen Bates · 1935
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
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Algorithmic learning in a random world , volume 29
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
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Normalized nonconformity measures for regression conformal prediction
Harris Papadopoulos, Alex Gammerman, and Volodya Vovk · 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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Efficient conformal regressors using bagged neural nets
Ulf Johansson, Cecilia Sönströd, and Henrik Linusson · 2015
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2016
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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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Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
Mauricio Sadinle, Jing Lei, and Larry Wasserman · 2017
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel Candes · 2019
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Rxrx1: An image set for cellular morphological variation across many experimental batches
J. Taylor, B. Earnshaw, B. Mabey, M. Victors, and J. Yosinski · 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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The iwildcam 2020 competition dataset
Sara Beery, Elijah Cole, and Arvi Gjoka · 2020
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Flexible distribution-free conditional predictive bands using density estimators
Rafael Izbicki, Gilson Shimizu, and Rafael Stern · 2020
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Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2020
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Classification with valid and adaptive coverage
Yaniv Romano, Matteo Sesia, and Emmanuel Candes · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
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Uncertainty sets for image classifiers using conformal prediction
Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, and Jitendra Malik · 2021
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Set-valued classification – overview via a unified framework, 2021
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, and Titouan Lorieul · 2021
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Rdumb: A simple approach that questions our progress in continual test-time adaptation
Ori Press, Steffen Schneider, Matthias Kümmerer, and Matthias Bethge · 2023
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Robots that ask for help: Uncertainty alignment for large language model planners
Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, and Anirudha Majumdar · 2023
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Improving adaptive conformal prediction using self-supervised learning
Nabeel Seedat, Alan Jeffares, Fergus Imrie, and Mihaela van der Schaar · 2023
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Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization
Junha Song, Jungsoo Lee, In So Kweon, and Sungha Choi · 2023
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Advanced topics in statistical learning: Conformal prediction
Ryan Tibshirani · 2023
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Adaptive conformal inference under distribution shift
Isaac Gibbs and Emmanuel Candes · 2021
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Sita: Single image test-time adaptation
Ansh Khurana, Sujoy Paul, Piyush Rai, Soma Biswas, and Gaurav Aggarwal · 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, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 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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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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Practical adversarial multivalid conformal prediction
Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, and Aaron Roth · 2022
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Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
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Object pose estimation with statistical guarantees: Conformal keypoint detection and geometric uncertainty propagation
Heng Yang and Marco Pavone · 2023
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Not all distributional shifts are equal: Fine-grained robust conformal inference
Jiahao Ai and Zhimei Ren · 2024
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Robust validation: Confident predictions even when distributions shift
Maxime Cauchois, Suyash Gupta, Alnur Ali, and John C Duchi · 2024
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Large language model validity via enhanced conformal prediction methods
John Cherian, Isaac Gibbs, and Emmanuel Candes · 2024
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Closure: Fast quantification of pose uncertainty sets
Yihuai Gao, Yukai Tang, Han Qi, and Heng Yang · 2024
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Optimal aggregation of prediction intervals under unsupervised domain shift
Jiawei Ge, Debarghya Mukherjee, and Jianqing Fan · 2024
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Conformal inference for online prediction with arbitrary distribution shifts
Isaac Gibbs and Emmanuel J Candès · 2024
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Conformal alignment: Knowing when to trust foundation models with guarantees
Yu Gui, Ying Jin, and Zhimei Ren · 2024
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Deep neural networks tend to extrapolate predictably
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Conformal decision theory: Safe autonomous decisions from imperfect predictions
Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, and Jitendra Malik · 2024
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Language models with conformal factuality guarantees
Christopher Mohri and Tatsunori Hashimoto · 2024
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The entropy enigma: Success and failure of entropy minimization
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Conformal language modeling
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Integrating uncertainty awareness into conformalized quantile regression
Raphael Rossellini, Rina Foygel Barber, and Rebecca Willett · 2024
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Discounted adaptive online prediction
Zhiyu Zhang, David Bombara, and Heng Yang · 2024
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