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Several uncertainty estimation methods have been recently proposed for machine translation evaluation.
Funzione caratteristica di un fenomeno aleatorio
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Machine-learning applications of algorithmic randomness
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Quantile regression
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Heteroscedastic gaussian process regression
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Algorithmic learning in a random world , volume 29
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Inductive conformal prediction: Theory and application to neural networks
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Regression conformal prediction with nearest neighbours
Harris Papadopoulos, Vladimir Vovk, and Alexander Gammerman. 2011 · 2011
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Continuous measurement scales in human evaluation of machine translation
Yvette Graham. 2013 · 2013
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Regression trees for streaming data with local performance guarantees
Ulf Johansson, Cecilia Sönströd, Henrik Linusson, and Henrik Boström. 2014 · 2014
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. 2015 · 2015
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Exploring prediction uncertainty in machine translation quality estimation
Daniel Beck, Lucia Specia, and Trevor Cohn. 2016 · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017
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Results of the wmt18 metrics shared task: Both characters and embeddings achieve good performance
Qingsong Ma, Ondřej Bojar, and Yvette Graham. 2018 · 2018
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Results of the wmt19 metrics shared task: Segment-level and strong mt systems pose big challenges
Qingsong Ma, Johnny Tian-Zheng Wei, Ondřej Bojar, and Yvette Graham. 2019 · 2019
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Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel Candes. 2019 · 2019
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Single-model uncertainties for deep learning
Natasa Tagasovska and David Lopez-Paz. 2019 · 2019
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Conformal prediction under covariate shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya Ramdas. 2019 · 2019
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Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus. 2020 · 2020
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Mondrian conformal regressors
Henrik Boström and Ulf Johansson. 2020 · 2020
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How the washington post estimates outstanding votes for the 2020 presidential election
John Cherian and Lenny Bronner. 2020 · 2020
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Dangers of bayesian model averaging under covariate shift
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, and Andrew G Wilson. 2021 · 2021
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Deup: Direct epistemic uncertainty prediction
Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Ion Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, and Yoshua Bengio. 2021 · 2021
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Conformal prediction sets with limited false positives
Adam Fisch, Tal Schuster, Tommi Jaakkola, and Regina Barzilay. 2022 · 2022
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Selection by prediction with conformal p-values
Ying Jin and Emmanuel J Candès. 2022 · 2022
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Calibrated and sharp uncertainties in deep learning via density estimation
Volodymyr Kuleshov and Shachi Deshpande. 2022 · 2022
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Revisiting the evaluation of uncertainty estimation and its application to explore model complexity-uncertainty trade-off
Yukun Ding, Jinglan Liu, Jinjun Xiong, and Yiyu Shi. 2020 · 2020
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Bert-based conformal predictor for sentiment analysis
Lysimachos Maltoudoglou, Andreas Paisios, and Harris Papadopoulos. 2020 · 2020
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Results of the wmt20 metrics shared task
Nitika Mathur, Johnny Wei, Markus Freitag, Qingsong Ma, and Ondřej Bojar. 2020 · 2020
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Comet: A neural framework for mt evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
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With malice toward none: Assessing uncertainty via equalized coverage
Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel Candès. 2020 · 2020
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates. 2021 · 2021
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Evaluating and calibrating uncertainty prediction in regression tasks
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Fair conformal predictors for applications in medical imaging
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Conformal prediction: General case and regression
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer. 2022 · 2022
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Uncertainty estimation and reduction of pre-trained models for text regression
Yuxia Wang, Daniel Beck, Timothy Baldwin, and Karin Verspoor. 2022 · 2022
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Disentangling uncertainty in machine translation evaluation
Chrysoula Zerva, Taisiya Glushkova, Ricardo Rei, and André FT Martins. 2022 · 2022
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Statistical inference for fairness auditing
John J Cherian and Emmanuel J Candès. 2023 · 2023
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Conformal prediction with large language models for multi-choice question answering
Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, and Andrew Beam. 2023 · 2023
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Shauli Ravfogel, Yoav Goldberg, and Jacob Goldberger. 2023 · 2023
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Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation
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