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Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution.
Statistical reasoning with imprecise probabilities , volume 42
Peter Walley · 1991
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Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management
Stephen C Hora · 1996
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Inferences from multinomial data: learning about a bag of marbles
Peter Walley · 1996
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Uncertainty-based information: elements of generalized information theory , volume 15
George Klir and Mark Wierman · 1999
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Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002
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The naive credal classifier
Marco Zaffalon · 2002
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Building classification trees using the total uncertainty criterion
Joaquín Abellán and Serafín Moral · 2003
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Learning reliable classifiers from small or incomplete data sets: The naive credal classifier 2
Giorgio Corani and Marco Zaffalon · 2008
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Exploiting ‘subjective’annotations
Dennis Reidsma and Rieks op den Akker · 2008
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Cheap and fast–but is it good? evaluating non-expert annotations for natural language tasks
Rion Snow, Brendan O’connor, Dan Jurafsky, and Andrew Y Ng · 2008
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Utility data annotation with amazon mechanical turk
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Bayesian networks with imprecise probabilities: Theory and application to classification
Giorgio Corani, Alessandro Antonucci, and Marco Zaffalon · 2012
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The three sides of crowdtruth
Lora Aroyo and Chris Welty · 2014
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Truth is a lie: Crowd truth and the seven myths of human annotation
Lora Aroyo and Chris Welty · 2015
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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Cross-conformal predictors
Vladimir Vovk · 2015
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Crowdsourcing in computer vision
Adriana Kovashka, Olga Russakovsky, Li Fei-Fei, Kristen Grauman, et al · 2016
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Resolvable vs. irresolvable ambiguity: A new hybrid framework for dealing with uncertain ground truth
Mike Schaekermann, Edith Law, Alex C Williams, and William Callaghan · 2016
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Leave-one-out prediction intervals in linear regression models with many variables
Lukas Steinberger and Hannes Leeb · 2016
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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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Criteria of efficiency for set-valued classification
Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova, Ivan Petej, and Alex Gammerman · 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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Aleatoric and epistemic uncertainty with random forests
Mohammad Hossein Shaker and Eyke Hüllermeier · 2020
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Predictive inference with the jackknife+
Rina Foygel Barber, Emmanuel J Candès, Aaditya Ramdas, and Ryan J Tibshirani · 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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Conformal prediction using conditional histograms
Matteo Sesia and Yaniv Romano · 2021
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Learning optimal conformal classifiers
David Stutz, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, and Arnaud Doucet · 2021
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Learning from disagreement: A survey
Alexandra N Uma, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, and Massimo Poesio · 2021
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Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi · 2019
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A crowdsourced frame disambiguation corpus with ambiguity
Anca Dumitrache, FD Mediagroep, Lora Aroyo, and Chris Welty · 2019
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Inherent disagreements in human textual inferences
Ellie Pavlick and Tom Kwiatkowski · 2019
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Conformalized quantile regression
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Least ambiguous set-valued classifiers with bounded error levels
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Pitfalls of epistemic uncertainty quantification through loss minimisation
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Conformal prediction intervals for markov decision process trajectories
Thomas G Dietterich and Jesse Hostetler · 2022
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Conformal prediction sets with limited false positives
Adam Fisch, Tal Schuster, Tommi Jaakkola, and Regina Barzilay · 2022
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Quantification of credal uncertainty in machine learning: A critical analysis and empirical comparison
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Two contrasting data annotation paradigms for subjective nlp tasks
Paul Röttger, Bertie Vidgen, Dirk Hovy, and Janet Pierrehumbert · 2022
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Scaling and disagreements: Bias, noise, and ambiguity
Alexandra Uma, Dina Almanea, and Massimo Poesio · 2022
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Algorithmic Learning in a Random World
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Gavin Abercrombie, Verena Rieser, and Dirk Hovy · 2023
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On second-order scoring rules for epistemic uncertainty quantification
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Conformal prediction with partially labeled data
Alireza Javanmardi, Yusuf Sale, Paul Hofman, and Eyke Hüllermeier · 2023
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Is the volume of a credal set a good measure for epistemic uncertainty?
Yusuf Sale, Michele Caprio, and Eyke Höllermeier · 2023
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