@article{einstein,
author =       "Albert Einstein",
title =        "{Zur Elektrodynamik bewegter Körper}",
journal =      "Annalen der Physik",
volume =       "322",
number =       "10",

year =         "1905"}

@book{latexcompanion,
author    = "Michel Goossens and Frank Mittelbach and Alexander Samarin",
title     = "The \LaTeX\ Companion",
year      = "1993",
publisher = "Addison-Wesley"
}

% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
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	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",

	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {$L_1$}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},

  year={2007},

}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK},

}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},

    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},

	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005},

}

@article{ct1965,
  title={An algorithm for the machine calculation of complex {F}ourier series},
  author={Cooley, James W. and Tukey, John W.},
  journal={Mathematics of Computation},
  volume={19},
  number={90},
  year={1965},

}



@article{errors1,
	author = {Michael P. Perrone and Leon N. Cooper},
	year = {1993},
	month = {08},
    title = {When Networks Disagree: Ensemble Methods for Hybrid Neural Networks},
    isbn = {978-981-02-1814-0},
    journal = {Neural networks for speech and image processing}
}

@article{errors2,
	author = {David Opitz and Richard Maclin},
	year = {1999},

	title = {Popular Ensemble Methods: An Empirical Study},
	journal = {Journal of Artificial Intelligence Research},
	volume = {11},
}

@inproceedings{errors3,
	author = {Thomas G. Dietterich},
	year = {2000},

	title = {Ensemble Methods in Machine Learning},
	booktitle = {Multiple Classifier Systems}
}

@inproceedings{diversity,
	title={Neural Network Ensembles, Cross Validation, and Active Learning},
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	year={1994}
}

@inproceedings{kd,
	title={Distilling the Knowledge in a Neural Network},
	author={Geoffrey Hinton and Oriol Vinyals and Jeff Dean},
	booktitle={Conference on Neural Information Processing Systems Deep Learning Workshop},
	year={2014}
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@inproceedings{compression,
	author={Cristian Bucilua and Rich Caruana and Alexandru Niculescu-Mizil},
	title={Model Compression},
	booktitle={International Conference on Knowledge Discovery and Data Mining},
	year={2016},
}

@inproceedings{decomp,
	author={Stefan Depeweg and Jose Miguel Hernández-Lobato and Finale Doshi-Velez and Steffen Udluft},
	title={Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning},
	booktitle={International Conference on Learning Representations},
	year={2018},
}

@inproceedings{mcd,
	author={Yarin Gal and Zoubin Ghahramani},
	title={Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning},
	booktitle={International Conference on Machine Learning},
	year={2016},
}

@inproceedings{duku,
	author={Eyke Hullermeier and Willem Waegeman},
	title={Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods},
	booktitle={Machine Learning},
	year={2021},
}

@inproceedings{deepens,
	author={Balaji Lakshminarayanan and Alexander Pritzel and Charles Blundell},
	title={Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles},
	booktitle={Conference on Neural Information Processing Systems},
	year={2017},
}

@inproceedings{hyperdeepens,
	author={Florian Wenzel and Jasper Snoek and Dustin Tran and Rodolphe Jenatton},
	title={Hyperparameter Ensembles for Robustness and Uncertainty Quantification},
	booktitle={Conference on Neural Information Processing Systems},
	year={2020},
}

@inproceedings{scaling-nmt,
	author={Myle Ott and Sergey Edunov and David Grangier and Michael Auli},
	title={Scaling Neural Machine Translation},
	booktitle={Proceedings of the Third Conference on Machine Translation: Research Papers},
	year={2018},
}

@inproceedings{atten,
	author={Dzmitry Bahdanau and Kyunghyun Cho and Yoshua Bengio},
	title={Neural machine translation by jointly learning to align and translate},
	booktitle={International Conference on Learning Representations},
	year={2015},
}

@inproceedings{aiayn,
	author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
	title={Attention Is All You Need},
	booktitle={Conference on Neural Information Processing Systems},
	year={2017},
}

@inproceedings{vggtransformer,
	author={Abdelrahman Mohamed and Dmytro Okhonko and Luke Zettlemoyer},
	title={Transformers with Convolutional Context for ASR},
	booktitle={arXiv},
	year={2019},
}

@inproceedings{bpe,
	author={Rico Sennrich and Barry Haddow and Alexandra Birch},
	title={Neural Machine Translation of Rare Words with Subword Units},
	booktitle={Association for Computational Linguistics},
	year={2016},
}

@inproceedings{swa,
	author={Pavel Izmailov and Dmitrii Podoprikhin and Timur Garipov and Dmitry Vetrov and Andrew Gordon Wilson},
	title={Averaging Weights Leads to Wider Optima and Better Generalization},
	booktitle={Conference on Uncertainty in Artificial Intelligence},
	year={2018},
}

@inproceedings{swag,
	author={Wesley Maddox and Timur Garipov and Pavel Izmailov and Dmitry Vetrov and Andrew Gordon Wilson},
	title={A Simple Baseline for Bayesian Uncertainty in Deep Learning},
	booktitle={Conference on Neural Information Processing Systems},
	year={2019},
}

@inproceedings{cyclictransformer,
	author={Pavel Karpov and Guillaume Godin and Igor V. Tetko},
	title={A Transformer Model for Retrosynthesis},
	booktitle={International Conference on Artificial Neural Networks},
	year={2019},
}

@inproceedings{batchensemble,
	author={Yeming Wen and Dustin Tran and Jimmy Ba},
	title={BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning},
	booktitle={International Conference on Learning Representations},
	year={2020},
}

@inproceedings{temporalensemble,
	author={Jingjing Xie and Bing Xu and Zhang Chuang},
	title={Horizontal and Vertical Ensemble with Deep Representation for Classification},
	booktitle={International Conference on Machine Learning},
	year={2013},
}

@inproceedings{snapshot,
	author={Gao Huang and Yixuan Li and Geoff Pleiss and Zhuang Liu and John E. Hopcroft and Kilian Q. Weinberger},
	title={Snapshot Ensembles: Train 1, get M for free},
	booktitle={International Conference on Learning Representations},
	year={2017},
}

@inproceedings{modes,
	author={Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson},
	title={Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs},
	booktitle={Conference on Neural Information Processing Systems},
	year={2018},
}

@inproceedings{shift,
	author={Andrey Malinin and Neil Band and German Chesnokov and Yarin Gal and Mark J. F. Gales and Alexey Noskov and Andrey Ploskonosov and Liudmila Prokhorenkova and Ivan Provilkov and Vatsal Raina and Vyas Raina and Mariya Shmatova and Panos Tigas and Boris Yangel},
	title={Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks},
	booktitle={Conference on Neural Information Processing Systems Track on Datasets and Benchmarks},
	year={2021},
}

@inproceedings{structured,
	author={Andrey Malinin and Mark J. F. Gales},
	title={Uncertainty Estimation in Autoregressive Structured Prediction},
	booktitle={International Conference on Learning Representations},
	year={2021},
}

@inproceedings{priorn,
	author={Andrey Malinin and Mark J. F. Gales},
	title={Predictive Uncertainty Estimation via Prior Networks},
	booktitle={Conference on Neural Information Processing Systems},
	year={2018},
}

@inproceedings{endd,
	author={Andrey Malinin and Bruno Mlodozeniec and Mark J. F. Gales},
	title={Ensemble distribution distillation},
	booktitle={International Conference on Learning Representations},
	year={2020},
}

@inproceedings{mimo,
	author={Marton Havasi and Rodolphe Jenatton and Stanislav Fort and Jeremiah Zhe Liu and Jasper Snoek and Balaji Lakshminarayanan and Andrew M. Dai and Dustin Tran},
	title={Training independent subnetworks for robust prediction},
	booktitle={International Conference on Learning Representations},
	year={2021},
}

@inproceedings{cnd,
	author={Khe Chai Sim and William J Byrne and Mark J. F. Gales and Hichem Sahbi and Philip C. Woodland},
	title={Consensus Network Decoding for Statistical Machine Translation System Combination},
	booktitle={International Conference on Acoustics, Speech, and Signal Processing},
	year={2007},
}

@inproceedings{self,
	author={Linfeng Zhang and Jiebo Song and Anni Gao and Jingwei Chen and Chenglong Bao and Kaisheng Ma},
	title={Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation},
	booktitle={International Conference on Computer Vision},
	year={2019},
}

@inproceedings{selfmicro,
	author={Allen-Zhu, Zeyuan and Li, Yuanzhi},
	title={Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning},
	booktitle={arXiv},
	year={2021},
}

@inproceedings{seq-kd,
	author={Yoon Kim and Alexander M. Rush},
	title={Sequence-Level Knowledge Distillation},
	booktitle={Conference on Empirical Methods in Natural Language Processing},
	year={2016},
}

@inproceedings{seq-endd,
	author={Yassir Fathullah and Mark J. F. Gales and Andrey Malinin},
	title={Ensemble Distillation Approaches for Grammatical Error Correction},
	booktitle={International Conference on Acoustics, Speech, and Signal Processing},
	year={2021},
}


@InProceedings{s2d,
  title = 	 {Self-distribution distillation: efficient uncertainty estimation},
  author =       {Fathullah, Yassir and Gales, Mark J. F.},
  booktitle = 	 {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence},

  year = 	 {2022},

  volume = 	 {180},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {01--05 Aug},

  pdf = 	 {https://proceedings.mlr.press/v180/fathullah22a/fathullah22a.pdf},

  abstract = 	 {Deep learning is increasingly being applied in safety-critical domains. For these scenarios it is important to know the level of uncertainty in a model’s prediction to ensure appropriate decisions are made by the system. Deep ensembles are the de-facto standard approach to obtaining various measures of uncertainty. However, ensembles often significantly increase the resources required in the training and/or deployment phases. Approaches have been developed that typically address the costs in one of these phases. In this work we propose a novel training approach, self-distribution distillation (S2D), which is able to efficiently train a single model that can estimate uncertainties. Furthermore it is possible to build ensembles of these models and apply hierarchical ensemble distillation approaches. Experiments on CIFAR-100 showed that S2D models outperformed standard models and Monte-Carlo dropout. Additional out-of-distribution detection experiments on LSUN, Tiny ImageNet, SVHN showed that even a standard deep ensemble can be outperformed using S2D based ensembles and novel distilled models.}
}



@inproceedings{gen-student-th,
	author={Jeremy H. M. Wong and Mark J. F. Gales and Yu Wang},
	title={Sequence-Level Knowledge Distillation},
	booktitle={IEEE/ACM Transactions on Audio Speech and Language Processing},
	year={2016},
}

@inproceedings{multi-nmt,
	author={Xu Tan and Yi Ren and Di He and Tao Qin and Zhou Zhao and Tie-Yan Liu},
	title={Multilingual Neural Machine Translation with Knowledge Distillation},
	booktitle={International Conference on Learning Representations},
	year={2019},
}

@inproceedings{global,
	author={Khanh Nguyen and Hal {Daum{\'{e}} III}},
	title={Global Voices: Crossing Borders in Automatic News Summarization},
	booktitle={Conference on Empirical Methods in Natural Language Processing New Frontiers in Summarization Workshop},
	year={2019},
}

@inproceedings{sacrebleu,
	author={Matt Post},
	title={A Call for Clarity in Reporting {BLEU} Scores},
	booktitle={Association for Computational Linguistics},
	year={2018},
}

@misc{khresmoi,
	author={Ond{\v r}ej Du{\v s}ek and Jan Haji{\v c} and Jaroslava Hlav{\'a}{\v c}ov{\'a} and Jind{\v r}ich Libovick{\'y} and Pavel Pecina and Ale{\v s} Tamchyna and Zde{\v n}ka Ure{\v s}ov{\'a}},
	title={Khresmoi Summary Translation Test Data 2.0},
	howpublished={\url{http://hdl.handle.net/11234/1-2122}},
	note={{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL})},
	year={2017},
}

@misc{kftt,
	author = {Graham Neubig},
	title = {The {Kyoto} Free Translation Task},
	howpublished = {\url{http://www.phontron.com/kftt}},
	year = {2011}
}

@inproceedings{mtnt,
	author={Paul Michel and Graham Neubig},
	title={MTNT: A Testbed for Machine Translation of Noisy Text},
	booktitle={Conference on Empirical Methods in Natural Language Processing},
	year={2018},
}

@book{auroc,
	author={Chris Manning and Hinrich Schütze},
	title={Foundations of Statistical Natural Language Processing},
	publisher={MIT Press},
	year={1999},
}

@misc{dark,
    author={‪Geoffrey Hinton and Oriol Vinyals and Jeff Dean},
    title={Dark knowledge},
    howpublished={Invited talk at the BayLearn Bay Area Machine Learning Symposium},
    year={2014}
}

@inproceedings{fairseq,
  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
  year = {2019},
}

@inproceedings{adam,
	author={Diederik P. Kingma and Jimmy Ba},
	title={Adam: A Method for Stochastic Optimization},
	booktitle={International Conference for Learning Representations},
	year={2015},
}

@inproceedings{ryabin,
	author={Max Ryabinin and Andrey Malinin and Mark J. F. Gales},
	title={Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets},
	booktitle={Conference on Neural Information Processing Systems},
	year={2021},
}

@inproceedings{cyclic,
	author={Leslie N. Smith},
	title={Cyclical Learning Rates for Training Neural Networks},
	booktitle={Winter Conference on Applications of Computer Vision},
	year={2017},
}

@inproceedings{ood-training-1,
	author={Kimin Lee and Honglak Lee and Kibok Lee and Jinwoo Shin},
	title={Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples},
	booktitle={International Conference on Learning Representations},
	year={2018},
}

@inproceedings{ood-training-2,
	author={Andrey Malinin and Anton Ragni and Kate Knill and  Mark J. F. Gales},
	title={Incorporating Uncertainty into Deep Learning for Spoken Language Assessment},
	booktitle={Association for Computational Linguistics},
	year={2017},
}

@inproceedings{seq-al1,
    author={Yanyao Shen and Hyokun Yun and Zachary Lipton and Yakov Kronrod and Animashree Anandkumar},
    title={Deep Active Learning for Named Entity Recognition},
    booktitle={Proceedings of the 2nd Workshop on Representation Learning for {NLP}},
    year={2017}
}

@inproceedings{seq-al2,
    author={Puria Radmard and Yassir Fathullah and Aldo Lipani},
    title={Subsequence Based Deep Active Learning for Named Entity Recognition},
    booktitle={Association for Computational Linguistics},
    year={2021}
}

@article{ebrahimi2018adversarial,
  title={On adversarial examples for character-level neural machine translation},
  author={Javid Ebrahimi and Daniel Lowd and Dejing Dou},
  journal={arXiv},
  year={2018}
}

@inproceedings{vae,
  author    = {Diederik P. Kingma and Max Welling},
  title     = {Auto-Encoding Variational Bayes},
  booktitle = {International Conference on Learning Representations},
  year      = {2014},
}

@inproceedings{seq-kd-app1,
    title   = {End-to-End Speech-Translation with Knowledge Distillation: {FBK}@{IWSLT}2020},
    author = {Marco Gaido and Mattia A. Di Gangi and Matteo Negri and Marco Turchi},
    booktitle = {International Conference on Spoken Language Translation},
    year = {2020},
}

@inproceedings{seq-kd-app2,
    title={Multilingual Neural Machine Translation with Knowledge Distillation},
    author={Xu Tan and Yi Ren and Di He and Tao Qin and Tie-Yan Liu},
    booktitle={International Conference on Learning Representations},
    year={2019},
}

@inproceedings{seq-kd-app3,
    title={{T}iny{BERT}: Distilling {BERT} for Natural Language Understanding},
    author={Xiaoqi Jiao and Yichun Yin and Lifeng Shang and Xin Jiang and Xiao Chen and Linlin Li and Fang Wang and Qun Liu},
    booktitle={International Conference on Learning Representations},
    year={2019},
}

@inproceedings{mod-edd,
  author={Jakob Lindqvist and Amanda Olmin and Fredrik Lindsten and Lennart Svensson},
  booktitle={International Workshop on Machine Learning for Signal Processing (MLSP)}, 
  title={A General Framework for Ensemble Distribution Distillation}, 
  year={2020},
}

@techreport{minka2005divergence,
  title = {Divergence measures and message passing},
  author = {Minka, Tom and others},
  year = {2005},
  institution = {Technical report, Microsoft Research}
}

@inproceedings{tcp,
   title = {Addressing Failure Prediction by Learning Model Confidence},
   author = {Corbi\`{e}re, Charles and Thome, Nicolas and Bar-Hen, Avner and Cord, Matthieu and P\'{e}rez, Patrick},
   booktitle = {Advances in Neural Information Processing Systems 32},
   year = {2019},
}

@InProceedings{LDU,
author="Franchi, Gianni
and Yu, Xuanlong
and Bursuc, Andrei
and Aldea, Emanuel
and Dubuisson, Severine
and Filliat, David
and Brostow, Gabriel
and Ciss{\'e}, Moustapha
and Farinella, Giovanni Maria
and Hassner, Tal",
title="Latent Discriminant Deterministic Uncertainty",
booktitle="Computer Vision -- ECCV 2022",
year="2022",

address="Cham",

abstract="Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address these challenges in the context of autonomous driving perception tasks. Recently proposed Deterministic Uncertainty Methods (DUM) can only partially meet such requirements as their scalability to complex computer vision tasks is not obvious. In this work we advance a scalable and effective DUM for high-resolution semantic segmentation, that relaxes the Lipschitz constraint typically hindering practicality of such architectures. We learn a discriminant latent space by leveraging a distinction maximization layer over an arbitrarily-sized set of trainable prototypes. Our approach achieves competitive results over Deep Ensembles, the state of the art for uncertainty prediction, on image classification, segmentation and monocular depth estimation tasks. Our code is available at https://github.com/ENSTA-U2IS/LDU.",
isbn="978-3-031-19775-8"
}

@inproceedings{
kotelevskii2022nonparametric,
title={Nonparametric Uncertainty Quantification for Single Deterministic Neural Network},
author={Nikita Yurevich Kotelevskii and Aleksandr Artemenkov and Kirill Fedyanin and Fedor Noskov and Alexander Fishkov and Artem Shelmanov and Artem Vazhentsev and Aleksandr Petiushko and Maxim Panov},
booktitle={Advances in Neural Information Processing Systems},

year={2022},

}

@inproceedings{Amersfoort2020SimpleAS,
  title={Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network},
  author={Joost R. van Amersfoort and Lewis Smith and Yee Whye Teh and Yarin Gal},
  booktitle={International Conference on Machine Learning},
  year={2020}
}

@inproceedings{sngp,
 author = {Liu, Jeremiah and Lin, Zi and Padhy, Shreyas and Tran, Dustin and Bedrax Weiss, Tania and Lakshminarayanan, Balaji},
 booktitle = {Advances in Neural Information Processing Systems},

 title = {Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness},

 volume = {33},
 year = {2020}
}

@inproceedings{
pinto2022using,
title={Using Mixup as a Regularizer Can Surprisingly Improve Accuracy \& Out-of-Distribution Robustness},
author={Francesco Pinto and Harry Yang and Ser-Nam Lim and Philip Torr and Puneet K. Dokania},
booktitle={Advances in Neural Information Processing Systems},

year={2022},

}

@article{Mukhoti2021DeterministicNN,
  title={Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty},
  author={Jishnu Mukhoti and Andreas Kirsch and Joost R. van Amersfoort and Philip H. S. Torr and Yarin Gal},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.11582}
}


@inproceedings{Postels2021OnTP,
  title={On the Practicality of Deterministic Epistemic Uncertainty},
  author={Janis Postels and Mattia Segu and Tao Sun and Luc Van Gool and Fisher Yu and Federico Tombari},
  booktitle={International Conference on Machine Learning},
  year={2021}
}

@inproceedings{energy,
 author = {Liu, Weitang and Wang, Xiaoyun and Owens, John and Li, Yixuan},
 booktitle = {Advances in Neural Information Processing Systems},

 title = {Energy-based Out-of-distribution Detection},

 volume = {33},
 year = {2020}
}

@inproceedings{haoqi2022vim,

title = {ViM: Out-Of-Distribution with Virtual-logit Matching},

author = {Wang, Haoqi and Li, Zhizhong and Feng, Litong and Zhang, Wayne},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

year = {2022}

}


@InProceedings{Xia_2022_ACCV,
    author    = {Xia, Guoxuan and Bouganis, Christos-Savvas},
    title     = {Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data},
    booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
    month     = {December},
    year      = {2022},

}

@article{Xia2022OnTU,
  title={On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection},
  author={Guoxuan Xia and Christos-Savvas Bouganis},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.07517}
}

@inproceedings{Moon2020ConfidenceAwareLF,
  title={Confidence-Aware Learning for Deep Neural Networks},
  author={Jooyoung Moon and Jihyo Kim and Younghak Shin and Sangheum Hwang},
  booktitle={International Conference on Machine Learning},
  year={2020}
}

@article{yang2021oodsurvey,
  title={Generalized Out-of-Distribution Detection: A Survey},
  author={Yang, Jingkang and Zhou, Kaiyang and Li, Yixuan and Liu, Ziwei},
  journal={arXiv preprint arXiv:2110.11334},
  year={2021}
}
@inproceedings{
yang2022openood,
title={Open{OOD}: Benchmarking Generalized Out-of-Distribution Detection},
author={Jingkang Yang and Pengyun Wang and Dejian Zou and Zitang Zhou and Kunyuan Ding and Wenxuan Peng and Haoqi Wang and Guangyao Chen and Bo Li and Yiyou Sun and Xuefeng Du and Kaiyang Zhou and Wayne Zhang and Dan Hendrycks and Yixuan Li and Ziwei Liu},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},

}

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

}

@inproceedings{ovadia_trust,
 author = {Ovadia, Yaniv and Fertig, Emily and Ren, Jie and Nado, Zachary and Sculley, D. and Nowozin, Sebastian and Dillon, Joshua and Lakshminarayanan, Balaji and Snoek, Jasper},
 booktitle = {Advances in Neural Information Processing Systems},

 title = {Can you trust your model\textquotesingle s uncertainty?  Evaluating predictive uncertainty under dataset shift},

 volume = {32},
 year = {2019}
}

@article{Kim2021AUB,
  title={A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks},
  author={Jihyo Kim and Jiin Koo and Sangheum Hwang},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.00337}
}

@inproceedings{gustafsson2020evaluating,
  title={Evaluating scalable Bayesian deep learning methods for robust computer vision},
  author={Gustafsson, Fredrik K and Danelljan, Martin and Sch{\"o}n, Thomas B},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  year={2020}
}

@inproceedings{batchbald,
 author = {Kirsch, Andreas and van Amersfoort, Joost and Gal, Yarin},
 booktitle = {Advances in Neural Information Processing Systems},

 title = {BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning},

 volume = {32},
 year = {2019}
}

@InProceedings{pmlr-v70-gal17a,
  title = 	 {Deep {B}ayesian Active Learning with Image Data},
  author =       {Yarin Gal and Riashat Islam and Zoubin Ghahramani},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},

  year = 	 {2017},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},

  abstract = 	 {Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).}
}


@InProceedings{pmlr-v97-geifman19a,
  title = 	 {{S}elective{N}et: A Deep Neural Network with an Integrated Reject Option},
  author =       {Geifman, Yonatan and El-Yaniv, Ran},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},

  year = 	 {2019},

  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},


  abstract = 	 {We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated reject option. Existing rejection mechanisms are based mostly on a threshold over the prediction confidence of a pre-trained network. In contrast, SelectiveNet is trained to optimize both classification (or regression) and rejection simultaneously, end-to-end. The result is a deep neural network that is optimized over the covered domain. In our experiments, we show a consistently improved risk-coverage trade-off over several well-known classification and regression datasets, thus reaching new state-of-the-art results for deep selective classification.}
}


@inproceedings{
    notin2021improving,
    title={Improving black-box optimization in {VAE} latent space using decoder uncertainty},
    author={Pascal Notin and Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Yarin Gal},
    booktitle={Advances in Neural Information Processing Systems},

    year={2021},
}

@inproceedings{
abe2022deep,
title={Deep Ensembles Work, But Are They Necessary?},
author={Taiga Abe and E. Kelly Buchanan and Geoff Pleiss and Richard Zemel and John Patrick Cunningham},
booktitle={Advances in Neural Information Processing Systems},
year={2022},
}