
@article{adabi_2016_TighteningEntropicUncertainty,
  title = {Tightening the Entropic Uncertainty Bound in the Presence of Quantum Memory},
  author = {Adabi, F. and Salimi, S. and Haseli, S.},
  year = {2016},
  journal = {Physical Review A},
  shortjournal = {Phys. Rev. A},
  volume = {93},
  number = {6},
  pages = {062123},
  optIssn = {2469-9926, 2469-9934},
  optDoi = {10.1103/PhysRevA.93.062123}
}

@inproceedings{beluch_2018_PowerEnsemblesActive,
  title = {The {{Power}} of {{Ensembles}} for {{Active Learning}} in {{Image Classification}}},
  booktitle = {{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Beluch, William H. and Genewein, Tim and Nurnberger, Andreas and Kohler, Jan M.},
  year = {2018},
  publisher = {{IEEE}},
  optDoi = {10.1109/cvpr.2018.00976}
}

@misc{branchaud-charron_2021_CanActiveLearning,
  title = {Can {{Active Learning Preemptively Mitigate Fairness Issues}}?},
  author = {Branchaud-Charron, Frédéric and Atighehchian, Parmida and Rodríguez, Pau and Abuhamad, Grace and Lacoste, Alexandre},
  year = {2021},
  number = {arXiv:2104.06879},
  publisher = {{arXiv}}
}

@article{budd_2021_SurveyActiveLearning,
  title = {A {{Survey}} on {{Active Learning}} and {{Human-in-the-Loop Deep Learning}} for {{Medical Image Analysis}}},
  author = {Budd, Samuel and Robinson, Emma C. and Kainz, Bernhard},
  year = {2021},
  journal = {Medical Image Analysis},
  volume = {71},
  optDoi = {optDoi.org/10.1016/j.media.2021.102062},
  annotation = {\_eprint: 1910.02923}
}

@inproceedings{charpentier_2020_PosteriorNetworkUncertainty,
  title = {Posterior {{Network}}: {{Uncertainty Estimation}} without {{OOD Samples}} via {{Density-Based Pseudo-Counts}}},
  booktitle = {{{NeurIPS}}},
  author = {Charpentier, Bertrand and Zügner, Daniel and Günnemann, Stephan},
  year = {2020},
  pages = {1103--1130}
}

@misc{charpentier_2022_DisentanglingEpistemicAleatoric,
  title = {Disentangling {{Epistemic}} and {{Aleatoric Uncertainty}} in {{Reinforcement Learning}}},
  author = {Charpentier, Bertrand and Senanayake, Ransalu and Kochenderfer, Mykel and Günnemann, Stephan},
  year = {2022},
  number = {arXiv:2206.01558},
  publisher = {{arXiv}}
}

@misc{depeweg_2018_DecompositionUncertaintyBayesian,
  title = {Decomposition of {{Uncertainty}} in {{Bayesian Deep Learning}} for {{Efficient}} and {{Risk-sensitive Learning}}},
  author = {Depeweg, Stefan and Hernández-Lobato, José Miguel and Doshi-Velez, Finale and Udluft, Steffen},
  year = {2018},
  number = {arXiv:1710.07283},
  publisher = {{arXiv}}
}

@phdthesis{depeweg_2019_ModelingEpistemicAleatoric,
  title = {Modeling {{Epistemic}} and {{Aleatoric Uncertainty}} with {{Bayesian Neural Networks}} and {{Latent Variable}}},
  author = {Depeweg, Stefan},
  year = {2019},
  school = {{TU München}}
}

@article{dubois_1996_RepresentingPartialIgnorance,
  title = {Representing Partial Ignorance},
  author = {Dubois, Didier and Prade, Henri M. and Smets, Philippe},
  year = {1996},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans},
  volume = {26},
  number = {3},
  pages = {361--377}
}

@misc{egele_2022_AutoDEUQAutomatedDeep,
  title = {{{AutoDEUQ}}: {{Automated Deep Ensemble}} with {{Uncertainty Quantification}}},
  shorttitle = {{{AutoDEUQ}}},
  author = {Egele, Romain and Maulik, Romit and Raghavan, Krishnan and Lusch, Bethany and Guyon, Isabelle and Balaprakash, Prasanna},
  year = {2022},
  number = {arXiv:2110.13511},
  publisher = {{arXiv}}
}

@phdthesis{gal_2016_UncertaintyDeepLearning,
  title = {Uncertainty in {{Deep Learning}}},
  author = {Gal, Yarin},
  year = {2016},
  school = {{University of Cambridge}}
}

@misc{gal_2017_DeepBayesianActivea,
  title = {Deep {{Bayesian Active Learning}} with {{Image Data}}},
  author = {Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
  year = {2017},
  number = {arXiv:1703.02910},
  publisher = {{arXiv}}
}

@incollection{gao_2020_ReducingDistributionalUncertainty,
  title = {Reducing {{Distributional Uncertainty}} by {{Mutual Information Maximisation}} and {{Transferable Feature Learning}}},
  booktitle = {Computer {{Vision}} – {{ECCV}} 2020},
  author = {Gao, Jian and Hua, Yang and Hu, Guosheng and Wang, Chi and Robertson, Neil M.},
  editor = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
  year = {2020},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {12368},
  pages = {587--605},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  optDoi = {10.1007/978-3-030-58592-1_35},
  optIsbn = {978-3-030-58591-4 978-3-030-58592-1}
}

@inproceedings{grover_2019_UncertaintyAutoencodersLearning,
  title = {Uncertainty {{Autoencoders}}: {{Learning Compressed Representations}} via {{Variational Information Maximization}}},
  booktitle = {Proceedings of the 22nd {{International Conference}} on {{Artificial Intelligence}} and {{Statistics}} ({{AISTATS}}) 2019},
  author = {Grover, Aditya and Ermon, Stefano},
  year = {2019},
  pages = {11},
  eventtitle = {22nd {{International Conference}} on {{Artificial Intelligence}} and {{Statistics}} ({{AISTATS}})}
}

@misc{guo_2022_SurveyUncertaintyReasoning,
  title = {A {{Survey}} on {{Uncertainty Reasoning}} and {{Quantification}} for {{Decision Making}}: {{Belief Theory Meets Deep Learning}}},
  shorttitle = {A {{Survey}} on {{Uncertainty Reasoning}} and {{Quantification}} for {{Decision Making}}},
  author = {Guo, Zhen and Wan, Zelin and Zhang, Qisheng and Zhao, Xujiang and Chen, Feng and Cho, Jin-Hee and Zhang, Qi and Kaplan, Lance M. and Jeong, Dong H. and Jøsang, Audun},
  year = {2022},
  number = {arXiv:2206.05675},
  publisher = {{arXiv}}
}

@article{haas_2021_UncertaintyEstimationDeep,
  title = {Uncertainty {{Estimation}} for {{Deep Learning-Based Segmentation}} of {{Roads}} in {{Synthetic Aperture Radar Imagery}}},
  author = {Haas, Jarrod and Rabus, Bernhard},
  year = {2021},
  journal = {Remote Sensing},
  shortjournal = {Remote Sensing},
  volume = {13},
  number = {8},
  pages = {1472},
  optIssn = {2072-4292},
  optDoi = {10.3390/rs13081472}
}

@misc{houlsby_2011_BayesianActiveLearning,
  title = {Bayesian {{Active Learning}} for {{Classification}} and {{Preference Learning}}},
  author = {Houlsby, Neil and Huszár, Ferenc and Ghahramani, Zoubin and Lengyel, Máté},
  year = {2011},
  annotation = {\_eprint: 1112.5745}
}

@article{hullermeier_2021_AleatoricEpistemicUncertainty,
  title = {Aleatoric and {{Epistemic Uncertainty}} in {{Machine Learning}}: {{An Introduction}} to {{Concepts}} and {{Methods}}},
  author = {Hüllermeier, Eyke and Waegeman, Willem},
  year = {2021},
  journal = {Machine Learning},
  optDoi = {10.1007/s10994-021-05946-3}
}

@inproceedings{hullermeier_2022_QuantificationCredalUncertainty,
  title = {Quantification of {{Credal Uncertainty}} in {{Machine Learning}}: {{A Critical Analysis}} and {{Empirical Comparison}}},
  booktitle = {{{UAI}}},
  author = {Hüllermeier, Eyke and Destercke, Sébastien and Shaker, Mohammad Hossein},
  year = {2022}
}

@article{DBLP:journals/corr/abs-2012-12687,
  author    = {Joachim Sicking and
               Maram Akila and
               Maximilian Pintz and
               Tim Wirtz and
               Asja Fischer and
               Stefan Wrobel},
  title     = {Second-Moment Loss: {A} Novel Regression Objective for Improved Uncertainties},
  journal   = {CoRR},
  volume    = {abs/2012.12687},
  year      = {2020},
  optUrl       = {https://arxiv.org/abs/2012.12687},
  eprinttype = {arXiv},
  eprint    = {2012.12687}
}

@inproceedings{uai22,
author={E. H\"ullermeier and S. Destercke and M.H. Shaker},
title={Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison},
booktitle={Proc.\ UAI, 38th Conference on Uncertainty in Artificial Intelligence},
address={Eindhoven, Netherlands},
year=2022}

@article{hullermeier_2022_QUANTIFYINGALEATORICEPISTEMIC,
  title = {{{QUANTIFYING ALEATORIC AND EPISTEMIC UNCERTAINTY IN MACHINE LEARNING}}: {{WHY CONDITIONAL ENTROPY AND MUTUAL INFORMATION ARE INAPPROPRIATE MEASURES}}},
  author = {Hüllermeier, Eyke},
  year = {2022},
  pages = {7}
}

@inproceedings{huseljic_2020_SeparationAleatoricEpistemic,
  title = {Separation of {{Aleatoric}} and {{Epistemic Uncertainty}} in {{Deterministic Deep Neural Networks}}},
  booktitle = {{{ICPR}}},
  author = {Huseljic, Denis and Sick, Bernhard and Herde, Mark and Kottke, Daniel},
  year = {2020}
}

@misc{kapoor_2022_UncertaintyTemperingData,
  title = {On {{Uncertainty}}, {{Tempering}}, and {{Data Augmentation}} in {{Bayesian Classification}}},
  author = {Kapoor, Sanyam and Maddox, Wesley J. and Izmailov, Pavel and Wilson, Andrew Gordon},
  year = {2022},
  annotation = {\_eprint: 2203.16481}
}

@inproceedings{kendall_2017_WhatUncertaintiesWe,
  title = {What {{Uncertainties Do We Need}} in {{Bayesian Deep Learning}} for {{Computer Vision}}?},
  author = {Kendall, Alex and Gal, Yarin},
  year = {2017},
  booktitle = {{{NeurIPS}}},
  annotation = {\_eprint: 1703.04977}
}

@misc{kirsch_2021_TestDistributionAwareActive,
  title = {Test {{Distribution-Aware Active Learning}}: {{A Principled Approach Against Distribution Shift}} and {{Outliers}}},
  shorttitle = {Test {{Distribution-Aware Active Learning}}},
  author = {Kirsch, Andreas and Rainforth, Tom and Gal, Yarin},
  year = {2021},
  number = {arXiv:2106.11719},
  publisher = {{arXiv}}
}

@misc{kirsch_2022_MarginalJointCrossEntropies,
  title = {Marginal and {{Joint Cross-Entropies}} \& {{Predictives}} for {{Online Bayesian Inference}}, {{Active Learning}}, and {{Active Sampling}}},
  author = {Kirsch, Andreas and Kossen, Jannik and Gal, Yarin},
  year = {2022},
  number = {arXiv:2205.08766},
  publisher = {{arXiv}}
}

@misc{kirsch_2022_UnifyingApproachesData,
  title = {Unifying {{Approaches}} in {{Data Subset Selection}} via {{Fisher Information}} and {{Information-Theoretic Quantities}}},
  author = {Kirsch, Andreas and Gal, Yarin},
  year = {2022},
  number = {arXiv:2208.00549},
  publisher = {{arXiv}}
}

@inproceedings{kopetzki_2021_EvaluatingRobustnessPredictive,
  title = {Evaluating {{Robustness}} of {{Predictive Uncertainty Estimation}}: {{Are Dirichlet-based Models Reliable}}?},
  shorttitle = {Evaluating {{Robustness}} of {{Predictive Uncertainty Estimation}}},
  booktitle = {{{ICML}}},
  author = {Kopetzki, Anna-Kathrin and Charpentier, Bertrand and Zügner, Daniel and Giri, Sandhya and Günnemann, Stephan},
  year = {2021},
  pages = {5707--5718}
}

@misc{liu_2020_EffectiveVersionSpacea,
  title = {Effective {{Version Space Reduction}} for {{Convolutional Neural Networks}}},
  author = {Liu, Jiayu and Chiotellis, Ioannis and Triebel, Rudolph and Cremers, Daniel},
  year = {2020},
  number = {arXiv:2006.12456},
  publisher = {{arXiv}}
}

@article{ma_2011_MutualInformationCopula,
  title = {Mutual Information Is Copula Entropy},
  author = {Ma, Jian and Sun, Zengqi},
  year = {2011},
  journal = {Tsinghua Science and Technology},
  volume = {16},
  number = {1},
  pages = {51--54},
  optIssn = {1007-0214},
  optDoi = {10.1016/S1007-0214(11)70008-6},
  eventtitle = {Tsinghua {{Science}} and {{Technology}}}
}

@inproceedings{malinin_2018_PredictiveUncertaintyEstimation,
  title = {Predictive {{Uncertainty Estimation}} via {{Prior Networks}}},
  booktitle = {{{NeurIPS}}},
  author = {Malinin, Andrey and Gales, Mark},
  year = {2018},
  optPages = {7047--7058},
  annotation = {\_eprint: 1802.10501}
}

@inproceedings{malinin_2019_ReverseKLDivergenceTraining,
  title = {Reverse {{KL-Divergence Training}} of {{Prior Networks}}: {{Improved Uncertainty}} and {{Adversarial Robustness}}},
  author = {Malinin, Andrey and Gales, Mark},
  year = {2019},
  pages = {12},
  booktitle = {33rd {{Conference}} on {{Neural Information Processing Systems}} ({{NeurIPS}} 2019)}
}

@inproceedings{malinin_2020_EnsembleDistributionDistillation,
  title = {Ensemble {{Distribution Distillation}}},
  booktitle = {Proceedings of the 8th {{International Conference}} on {{Learning Representations}}},
  author = {Malinin, Andrey and Mlodozeniec, Bruno and Gales, Mark},
  year = {2020}
}

@misc{michelmore_2018_EvaluatingUncertaintyQuantification,
  title = {Evaluating {{Uncertainty Quantification}} in {{End-to-End Autonomous Driving Control}}},
  author = {Michelmore, Rhiannon and Kwiatkowska, Marta and Gal, Yarin},
  year = {2018},
  number = {arXiv:1811.06817},
  publisher = {{arXiv}}
}

@article{mobiny_2021_DropConnectEffectiveModeling,
  title = {{{DropConnect}} Is Effective in Modeling Uncertainty of {{Bayesian}} Deep Networks},
  author = {Mobiny, Aryan and Yuan, Pengyu and Moulik, Supratik K. and Garg, Naveen and Wu, Carol C. and Van Nguyen, Hien},
  year = {2021},
  journal = {Scientific Reports},
  shortjournal = {Sci Rep},
  volume = {11},
  optIssn = {2045-2322},
  optDoi = {10.1038/s41598-021-84854-x}
}

@misc{mukhoti_2022_DeepDeterministicUncertainty,
  title = {Deep {{Deterministic Uncertainty}}: {{A Simple Baseline}}},
  shorttitle = {Deep {{Deterministic Uncertainty}}},
  author = {Mukhoti, Jishnu and Kirsch, Andreas and van Amersfoort, Joost and Torr, Philip H. S. and Gal, Yarin},
  options = {useprefix=true},
  year = {2022},
  number = {arXiv:2102.11582},
  publisher = {{arXiv}}
}

@misc{psaros_2022_UncertaintyQuantificationScientificb,
  title = {Uncertainty {{Quantification}} in {{Scientific Machine Learning}}: {{Methods}}, {{Metrics}}, and {{Comparisons}}},
  shorttitle = {Uncertainty {{Quantification}} in {{Scientific Machine Learning}}},
  author = {Psaros, Apostolos F. and Meng, Xuhui and Zou, Zongren and Guo, Ling and Karniadakis, George Em},
  year = {2022},
  number = {arXiv:2201.07766},
  publisher = {{arXiv}}
}

@article{rajaraman_2022_UncertaintyQuantificationSegmenting,
  title = {Uncertainty {{Quantification}} in {{Segmenting Tuberculosis-Consistent Findings}} in {{Frontal Chest X-rays}}},
  author = {Rajaraman, Sivaramakrishnan and Zamzmi, Ghada and Yang, Feng and Xue, Zhiyun and Jaeger, Stefan and Antani, Sameer K.},
  year = {2022},
  journal = {Biomedicines},
  shortjournal = {Biomedicines},
  volume = {10},
  number = {6},
  pages = {1323},
  optIssn = {2227-9059},
  optDoi = {10.3390/biomedicines10061323}
}

@article{senge_2014_ReliableClassificationLearning,
  title = {Reliable Classification: {{Learning}} Classifiers That Distinguish Aleatoric and Epistemic Uncertainty},
  shorttitle = {Reliable Classification},
  author = {Senge, Robin and Bösner, Stefan and Dembczyński, Krzysztof and Haasenritter, Jörg and Hirsch, Oliver and Donner-Banzhoff, Norbert and Hüllermeier, Eyke},
  year = {2014},
  journal = {Information Sciences},
  shortjournal = {Information Sciences},
  volume = {255},
  pages = {16--29},
  optIssn = {00200255},
  optDoi = {10.1016/j.ins.2013.07.030}
}

@inproceedings{shelmanov_2021_ActiveLearningSequence,
  title = {Active {{Learning}} for {{Sequence Tagging}} with {{Deep Pre-trained Models}} and {{Bayesian Uncertainty Estimates}}},
  booktitle = {Proceedings of the 16th {{Conference}} of the {{EACL}}},
  author = {Shelmanov, Artem and Puzyrev, Dmitri and Kupriyanova, Lyubov and Belyakov, Denis and Larionov, Daniil and Khromov, Nikita and Kozlova, Olga and Artemova, Ekaterina and Dylov, Dmitry V. and Panchenko, Alexander},
  year = {2021},
  pages = {1698--1712},
  publisher = {{Association for Computational Linguistics}},
  location = {{Online}},
  optDoi = {10.18653/v1/2021.eacl-main.145},
  eventtitle = {Proceedings of the 16th {{Conference}} of the {{EACL}}}
}

@inproceedings{smith_2011_QuantifyingInformationFlow,
  title = {Quantifying {{Information Flow Using Min-Entropy}}},
  booktitle = {2011 {{Eighth International Conference}} on {{Quantitative Evaluation}} of {{SysTems}}},
  author = {Smith, Geoffrey},
  year = {2011},
  pages = {159--167},
  publisher = {{IEEE}},
  location = {{Aachen, Germany}},
  optDoi = {10.1109/QEST.2011.31},
  eventtitle = {2011 {{Eighth International Conference}} on {{Quantitative Evaluation}} of {{Systems}} ({{QEST}})},
  optIsbn = {978-1-4577-0973-9}
}

@misc{smith_2018_UnderstandingMeasuresUncertaintya,
  title = {Understanding {{Measures}} of {{Uncertainty}} for {{Adversarial Example Detection}}},
  author = {Smith, Lewis and Gal, Yarin},
  year = {2018},
  number = {arXiv:1803.08533},
  publisher = {{arXiv}}
}

@article{sun_2014_InformationEntropyMutual,
  title = {Information {{Entropy}} and {{Mutual Information-based Uncertainty Measures}} in {{Rough Set Theory}}},
  author = {Sun, Lin and Xu, Jiucheng},
  year = {2014},
  journal = {Applied Mathematics \& Information Sciences},
  shortjournal = {Appl. Math. Inf. Sci.},
  volume = {8},
  number = {4},
  pages = {1973--1985},
  optIssn = {1935-0090, 2325-0399},
  optDoi = {10.12785/amis/080456}
}

@article{winkler_2022_StochasticControlBayesian,
  title = {Stochastic {{Control}} for {{Bayesian Neural Network Training}}},
  author = {Winkler, Ludwig and Ojeda, César and Opper, Manfred},
  year = {2022},
  journal = {Entropy},
  shortjournal = {Entropy},
  volume = {24},
  optIssn = {1099-4300},
  optDoi = {10.3390/e24081097}
}

@misc{woo_2022_AnalyticMutualInformation,
  title = {Analytic {{Mutual Information}} in {{Bayesian Neural Networks}}},
  author = {Woo, Jae Oh},
  year = {2022},
  number = {arXiv:2201.09815},
  publisher = {{arXiv}}
}

@inproceedings{wu_2020_EnsembleApproachesUncertainty,
  title = {Ensemble {{Approaches}} for {{Uncertainty}} in {{Spoken Language Assessment}}},
  booktitle = {Interspeech 2020},
  author = {Wu, Xixin and Knill, Kate M. and Gales, Mark J.F. and Malinin, Andrey},
  year = {2020},
  pages = {3860--3864},
  publisher = {{ISCA}},
  optDoi = {10.21437/Interspeech.2020-2238},
  eventtitle = {Interspeech 2020}
}

@phdthesis{malinin_2019_UncertaintyEstimationDeep,
  title = {Uncertainty {{Estimation}} in {{Deep Learning}} with {{Application}} to {{Spoken Language Assessment}}},
  author = {Malinin, Andrey},
  year = {2019},
  school = {{University of Cambridge}}
}

@misc{daxberger_2021_LaplaceReduxEffortless,
  title = {Laplace {{Redux}} – {{Effortless Bayesian Deep Learning}}},
  author = {Daxberger, Erik and Kristiadi, Agustinus and Immer, Alexander and Eschenhagen, Runa and Bauer, Matthias and Hennig, Philipp},
  year = {2021},
  annotation = {\_eprint: 2106.14806}
}

@inproceedings{gal_2016_DropoutBayesianApproximation,
  title = {Dropout as a {{Bayesian Approximation}}: {{Representing Model Uncertainty}} in {{Deep Learning}}},
  booktitle = {Proceedings of the 33rd {{International Conference}} on {{Machine Learning}}},
  author = {Gal, Yarin and Ghahramani, Zoubin},
  year = {2016},
  annotation = {\_eprint: 1506.02142}
}

@inproceedings{lakshminarayanan_2017_SimpleScalablePredictive,
  title = {Simple and {{Scalable Predictive Uncertainty Estimation}} Using {{Deep Ensembles}}},
  booktitle = {{{NeurIPS}}},
  author = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
  year = {2017}
}

@book{gelman_2021_BayesianDataAnalysis,
  title = {Bayesian {{Data Analysis}}},
  author = {Gelman, Andrew and Carlin, John and Stern, Hal and Dunson, David and Vehtari, Aki and Rubin, Donald},
  year = {2021},
  publisher = {{CRC Press}}
}

@book{cover_2006_ElementsInformationTheory,
  title = {Elements of {{Information}}  {{Theory}}},
  author = {Cover, Thomas M and Thomas, Joy A},
  year = {2006},
  edition = {2},
  publisher = {{Wiley}}
}

@article{shannon_1948_MathematicalTheoryCommunication,
  title = {A {{Mathematical Theory}} of {{Communication}}},
  author = {Shannon, C. E.},
  year = {1948},
  journal = {The Bell System Technical Journal},
  volume = {27},
  pages = {379--423}
}

@book{ash_1965_InformationTheory,
  title = {Information {{Theory}}},
  author = {Ash, Robert B.},
  year = {1965},
  publisher = {{Dover Publications}}
}

@article{andrieu_2003_IntroductionMCMCMachine,
  title = {An {{Introduction}} to {{MCMC}} for {{Machine Learning}}},
  author = {Andrieu, Christophe and De Freitas, Nano and Doucet, Arnaud and Jordan, Michael I.},
  year = {2003},
  journal = {Machine Learning},
  volume = {50},
  pages = {5--43}
}

@inproceedings{ashukha_2020_PitfallsInDomainUncertaintya,
  title = {Pitfalls of {{In-Domain Uncertainty Estimation}} and {{Ensembling}} in {{Deep Learning}}},
  booktitle = {{{ICLR}}},
  author = {Ashukha, Arsenii and Lyzhov, Alexander and Molchanov, Dmitry and Vetrov, Dmitry},
  year = {2020}
}

@inproceedings{ovadia_can_2019,
  title = {Can {{You Trust Your Model}}'s {{Uncertainty}}? {{Evaluating Predictive Uncertainty Under Dataset Shift}}},
  booktitle = {Advances in {{Neural Information Processing Systems}} 32 ({{NeurIPS}} 2019)},
  author = {Ovadia, Yaniv and Fertig, Emily and Ren, Jie and Nado, Zachary and Sculley, D. and Nowozin, Sebastian and Dillon, Joshua V. and Lakshminarayanan, Balaji and Snoek, Jasper},
  year = {2019},
  annotation = {\_eprint: 1906.02530}
}

@misc{abe_2022_DeepEnsemblesWork,
  title = {Deep {{Ensembles Work}}, {{But Are They Necessary}}?},
  author = {Abe, Taiga and Buchanan, E. Kelly and Pleiss, Geoff and Zemel, Richard and Cunningham, John P.},
  year = {2022},
  number = {arXiv:2202.06985},
  publisher = {{arXiv}}
}



@unpublished{turkoglu_2022_FiLMEnsembleProbabilisticDeep,
  title = {{FiLM-Ensemble}: {{Probabilistic Deep Learning}} via {{Feature-wise Linear Modulation}}},
  shorttitle = {{{FiLM-Ensemble}}},
  author = {Turkoglu, Mehmet Ozgur and Becker, Alexander and Gündüz, Hüseyin Anil and Rezaei, Mina and Bischl, Bernd and Daudt, Rodrigo Caye and D'Aronco, Stefano and Wegner, Jan Dirk and Schindler, Konrad},
  year = {2022},
  note = {Accepted at the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS)},
  number = {arXiv:2206.00050}
}

@misc{dua_2019_UCI,
    author = "Dua, Dheeru and Graff, Casey",
    year = "2017",
    title = "{UCI} Machine Learning Repository",
    url ="http://archive.ics.uci.edu/ml",
    institution = "University of California, Irvine, School of Information and Computer Sciences" 
} 

@techreport{Krizhevsky2009learning,
    author = {Alex Krizhevsky},
    title = {Learning multiple layers of features from tiny images},
    institution = {University of Toronto},
    year = {2009}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@inproceedings{huang_2017_DenselyConnectedConvolutionala,
  title = {Densely {{Connected Convolutional Networks}}},
  booktitle = {2017 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q.},
  year = {2017},
  pages = {2261--2269},
  publisher = {{IEEE}},
  location = {{Honolulu, HI}},
  optDoi = {10.1109/CVPR.2017.243},
  eventtitle = {2017 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  optIsbn = {978-1-5386-0457-1}
}

@inproceedings{lecun_1998_GradientBasedLearningApplied,
  title = {Gradient-{{Based Learning Applied}} to {{Document Recognition}}},
  booktitle = {Proceedings of the {{IEEE}}},
  author = {LeCun, Yann and Bottou, Léon and Bengio, Yoshua and Haffner, Patrick},
  year = {1998}
}

@article{seidenfeld1986entropy,
  title={Entropy and uncertainty},
  author={Seidenfeld, Teddy},
  journal={Philosophy of Science},
  volume={53},
  number={4},
  pages={467--491},
  year={1986},
  publisher={Cambridge University Press}
}

@misc{wilson_2020_CaseBayesianDeep,
  title = {The {{Case}} for {{Bayesian Deep Learning}}},
  author = {Wilson, Andrew Gordon},
  year = {2020},
  annotation = {\_eprint: 2001.10995}
}

@inproceedings{kristiadi_2022_BeingBitFrequentist,
  title = {Being a {{Bit Frequentist Improves Bayesian Neural Networks}}},
  booktitle = {{{AISTATS}}},
  author = {Kristiadi, Agustinus and Hein, Matthias and Hennig, Philipp},
  year = {2022}
}

@inproceedings{lee_2021_SUNRISESimpleUnified,
  title = {{{SUNRISE}}: {{A Simple Unified Framework}}  for {{Ensemble Learning}} in {{Deep Reinforcement Learning}}},
  booktitle = {{{ICML}}},
  author = {Lee, Kimin and Laskin, Michael and Srinivas, Aravind and Abbeel, Pieter},
  year = {2021}
}

@misc{hinton_2012_RMSPropDivideGradient,
  title = {{RMSP}rop: {{Divide}} the {{Gradient}} by a {{Running Average}} of {{Its Recent Magnitude}} ({{Lecture}} on {{Neural Networks}} for {{Machine Learning}})},
  author = {Hinton, Geoffrey and Srivastava, Nitish and Swersky, Kevin},
  year = {2012}
}

@inproceedings{guo_2017_CalibrationModernNeural,
  title = {On {{Calibration}} of {{Modern Neural Networks}}},
  booktitle = {Proceedings of the 34 Th {{International Conference}} on {{Machine Learning}}},
  author = {Guo, Chuan and Pleiss, Geoff and Sun, Yu and Weinberger, Kilian Q.},
  year = {2017},
}

@misc{nixon_2019_MeasuringCalibrationDeep,
  title = {Measuring {{Calibration}} in {{Deep Learning}}},
  author = {Nixon, Jeremy and Dusenberry, Mike and Jerfel, Ghassen and Nguyen, Timothy and Liu, Jeremiah and Zhang, Linchuan and Tran, Dustin},
  year = {2019},
}

@article{breiman_2001_RandomForests,
  title = {Random {{Forests}}},
  author = {Breiman, Leo},
  year = {2001},
  journal = {Machine Learning},
  volume = {45},
  number = {1},
  pages = {5--32},
}

@article{bissiri_2016,
   title={A general framework for updating belief distributions},
   volume={78},
   optISSN={1369-7412},
   optUrl={http://dx.doi.org/10.1111/rssb.12158},
   optDoi={10.1111/rssb.12158},
   number={5},
   journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
   publisher={Wiley},
   author={Bissiri, P. G. and Holmes, C. C. and Walker, S. G.},
   year={2016},
   pages={1103–1130} }

@article{pan_2019_NovelBeliefEntropy,
  title = {A {{Novel Belief Entropy}} for {{Measuring Uncertainty}} in {{Dempster-Shafer Evidence Theory Framework Based}} on {{Plausibility Transformation}} and {{Weighted Hartley Entropy}}},
  author = {Pan, Qian and Zhou, Deyun and Tang, Yongchuan and Li, Xiaoyang and Huang, Jichuan},
  year = {2019},
  journal = {Entropy},
  shortjournal = {Entropy},
  volume = {21},
  number = {2},
  pages = {163},
  issn = {1099-4300},
  optDoi = {10.3390/e21020163}
}

@inproceedings{tan_2019_EfficientNetRethinkingModel,
  title = {{{EfficientNet}}: {{Rethinking Model Scaling}} for {{Convolutional Neural Networks}}},
  shorttitle = {{{EfficientNet}}},
  booktitle = {{{ICML}}},
  author = {Tan, Mingxing and Le, Quoc V.},
  year = {2019}
}

@article{ebrahimi2010information,
  title={Information measures in perspective},
  author={Ebrahimi, Nader and Soofi, Ehsan S and Soyer, Refik},
  journal={International Statistical Review},
  volume={78},
  number={3},
  pages={383--412},
  year={2010},
  publisher={Wiley Online Library}
}


@inproceedings{loshchilov_2017_SGDRStochasticGradient,
  title = {{{SGDR}}: {{Stochastic Gradient Descent}} with {{Warm Restarts}}},
  shorttitle = {{{SGDR}}},
  booktitle = {{{ICLR}}},
  author = {Loshchilov, Ilya and Hutter, Frank},
  year = {2017},
}

@inproceedings{naeini_2015_ObtainingWellCalibrated,
  title = {Obtaining {{Well Calibrated Probabilities Using Bayesian Binning}}},
  booktitle = {Proceedings of the {{AAAI Conference}} on {{Artificial Intelligence}}},
  author = {Naeini, Mahdi Pakdaman and Cooper, Gregory and Hauskrecht, Milos},
  year = {2015},
  volume = {29},
  optDoi = {10.1609/aaai.v29i1.9602}
}

@article{pal_1992_UncertaintyMeasuresEvidential,
  title = {Uncertainty Measures for Evidential Reasoning {{I}}: {{A}} Review},
  shorttitle = {Uncertainty Measures for Evidential Reasoning {{I}}},
  author = {Pal, Nikhil R. and Bezdek, James C. and Hemasinha, Rohan},
  year = {1992},
  journal = {International Journal of Approximate Reasoning},
  shortjournal = {International Journal of Approximate Reasoning},
  volume = {7},
  number = {3-4},
  pages = {165--183},
  opIssn = {0888613X},
  optDoi = {10.1016/0888-613X(92)90009-O}
}

@inproceedings{bronevich2008axioms,
  title={Axioms for uncertainty measures on belief functions and credal sets},
  author={Bronevich, Andrey and Klir, George J},
  booktitle={NAFIPS},
  pages={1--6},
  year={2008},
  organization={IEEE}
}

@article{pal1993uncertainty,
  title={Uncertainty measures for evidential reasoning II: A new measure of total uncertainty},
  author={Pal, Nikhil R and Bezdek, James C and Hemasinha, Rohan},
  journal={International Journal of Approximate Reasoning},
  volume={8},
  number={1},
  pages={1--16},
  year={1993},
  publisher={Elsevier}
}

@inproceedings{kristiadi_2020_BeingBayesianEven,
  title = {Being {{Bayesian}}, {{Even Just}} a {{Bit}}, {{Fixes Overconfidence}} in {{ReLU Networks}}},
  booktitle = {{{ICML}}},
  author = {Kristiadi, Agustinus and Hein, Matthias and Hennig, Philipp},
  year = {2020},
  publisher = {{arXiv}}
}

@article{dubois_1996_RepresentingPartialIgnorancea,
  title = {Representing Partial Ignorance},
  author = {Dubois, D. and Prade, H. and Smets, P.},
  year = {1996},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans},
  shortjournal = {IEEE Trans. Syst., Man, Cybern. A},
  volume = {26},
  number = {3},
  pages = {361--377}
}

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@inproceedings{paszke_2019_PyTorchImperativeStyle,
  title = {{{PyTorch}}: {{An Imperative Style}}, {{High-Performance Deep Learning Library}}},
  booktitle = {Proceedings of the 33rd {{Conference}} on {{Neural Information Processing Systems}} ({{NeurIPS}} 2019)},
  author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  year = {2019}
}

@misc{lightningai_2023_PyTorchLightningTrain,
  title = {{{PyTorch Lightning}}: {{Train}} and {{Deploy PyTorch}} at {{Scale}}},
  author = {{Lightning AI}},
  year = {2023}
}