
% Example citations

@incollection{Bengio+chapter2007,
author = {Bengio, Yoshua and LeCun, Yann},
booktitle = {Large Scale Kernel Machines},
publisher = {MIT Press},
title = {Scaling Learning Algorithms Towards {AI}},
year = {2007}
}

@article{Hinton06,
author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
journal = {Neural Computation},
pages = {1527--1554},
title = {A Fast Learning Algorithm for Deep Belief Nets},
volume = {18},
year = {2006}
}

@book{goodfellow2016deep,
title={Deep learning},
author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
volume={1},
year={2016},
publisher={MIT Press}
}

% CORE CITATIONS:

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

% Knowledge distillation
@inproceedings{kd,
	title = {Distilling the Knowledge in a Neural Network},
	author = {Hinton, Geoffrey E. and Vinyals, Oriol and Dean, Jeff},
	booktitle = {Conference on Neural Information Processing Systems},
	year = {2014}
}

% Ensemble distribution distillation
@inproceedings{en2d,
	title = {Ensemble Distribution Distillation},
	author = {Malinin, Andrey and Mlodozeniec, Bruno and Gales, Mark J. F},
	booktitle = {International Conference on Learning Representations},
	year = {2020}
}

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

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

% Large scale uncertainty estimation
@inproceedings{large-scale-unc,
	title = {Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets},
	author = {Ryabinin, Max and Malinin, Andrey and Gales, Mark J. F.},
	booktitle = {arXiv preprint arXiv:2001.10995},
	year = {2021}
}

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

@inproceedings{batchens,
	title = {Batchensemble: an alternative approach to efficient ensemble and lifelong learning},
	author = {Wen, Yeming and Tran, Dustin and Ba, Jimmy},
	booktitle = {International Conference on Machine Learning},
	year = {2020}
}

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

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

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

% Dropout
@inproceedings{dropout,
	title = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
	author = {Srivastava, Nitish and Hinton, Geoffrey E. and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
	booktitle = {Journal of Machine Learning Research},
	year = {2014}
}

% Densenet
@inproceedings{densenet,
	title = {Densely Connected Convolutional Networks},
	author = {Huang, Gao and Liu, Zhuang and Maaten, Laurens van der and Weinberger, Kilian Q.},
	booktitle = {Conference on Computer Vision and Pattern Recognition},
	year = {2017}
}

% Wideresnet
@inproceedings{wideresnet,
	title = {Wide Residual Networks},
	author = {Zagoruyko, Sergey and Komodakis, Nikos},
	booktitle = {The British Machine Vision Conference},
	year = {2016}
}

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

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

% Bald
@inproceedings{bald,
	title = {Bayesian Active Learning for Classification and Preference Learning},
	author={Neil Houlsby and Ferenc Husz{\'a}r and Zoubin Ghahramani and M{\'a}t{\'e} Lengyel},
	booktitle = {arXiv preprint arXiv:1112.5745},
	year={2011},
}

% Batchbald
@inproceedings{batchbald,
	title = {BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning},
	author = {Kirsch, Andreas and Amersfoort, Joost van and Gal, Yarin},
	booktitle = {Neural Information Processing Systems},
	year = {2019}
}

% Pitfalls
@inproceedings{pitfalls,
	title = {Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning},
	author = {Ashukha, Arsenii and Lyzhov, Alexander and Molchanov, Dmitry and Vetrov, Dmitry},
	booktitle = {International Conference on Learning Representations},
	year = {2020}
}

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

% Single shot
@inproceedings{single-shot,
	title = {Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences},
	author = {Seo, Seonguk and Seo, Paul Hongsuck and Han, Bohyung},
	booktitle = {Conference on Computer Vision and Pattern Recognition},
	year = {2019}
}

% Online kd
@inproceedings{onlinekd,
	title = {Online Knowledge Distillation via Collaborative Learning},
	author = {Guo, Qiushan and Wang, Xinjiang and Wu, Yichao and Yu, Zhipeng and Liang, Ding and Hu, Xiaolin and Luo, Ping},
	booktitle = {Conference on Computer Vision and Pattern Recognition},
	year = {2020}
}

% Born again networks
@inproceedings{ban,
	title = {Born-Again Neural Networks},
	author = {Furlanello, Tommaso and Lipton, Zachary. C and Tschannen, Michael and Itti, Laurent and Anandkumar, Anima},
	booktitle = {International Conference on Machine Learning},
	year = {2018}
}

% Detection metrics
@inproceedings{aupr,
	author = {Fawcett, Tom},
	title = {An introduction to roc analysis},
	booktitle = {Pattern Recognition Letters},
	year = 2006,
}
@book{auroc,
	author = {Chris Manning and Hinrich Schütze},
	title = {Foundations of Statistical Natural Language Processing},
	publisher = {MIT Press},
	year = 1999,
}

% Sota neural networks
@inproceedings{speechrec,
	title= {Deep Neural Networks for Acoustic Modeling in Speech Recognition: The shared Views of Four Research Groups},
	author = {Hinton, Geoffrey E. and Li, Deng and Yu, Dong and Dahl, George E. and Mohamed, Abdel-rahman and Jaitly, Navdeep and Senior, Andrew and Vanhoucke, Vincent and Nguyen, Patrick and Sainath, Tara N. and Kingsbury, Brian},
	booktitle = {IEEE Signal Processing Magazine},
	year = {2012}
}

@inproceedings{cvision,
	title= {Imagenet Classification with Deep Convolutional Neural Networks},
	author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E. },
	booktitle = {Conference on Neural Information Processing Systems},
	year = {2012}
}

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


% Uncertainty applications
@inproceedings{biometric,
	title= {Facenet: A Unified Embedding for Face Recognition and Clustering},
	author = {Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},
	booktitle = {Conference on Computer Vision and Pattern Recognition},
	year = {2015}
}

@inproceedings{medical,
	title= {Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease},
	author = {De Fauw, Jeffrey and Ledsam, Joseph R. and Romera-Paredes, Bernardino and Nikolov, Stanislav and Tomasev, Nenad and Blackwell, Sam and Askham, Harry and Glorot, Xavier and O’Donoghue, Brendan and Visentin, Daniel and van den Driessche, George and Lakshminarayanan, Balaji and Meyer, Clemens and Mackinder, Faith and Bouton, Simon and Ayoub, et al, Kareem},
	booktitle = {Nature Medicine},
	year = {2018}
}

@inproceedings{aisafety,
	title = {Concrete problems in AI safety},
	author = {Amodei, Dario and Olah, Chris and Steinhardt, Jacob and Christiano, Paul F. and Schulman, John and Mané, Dan},
	booktitle = {arXiv preprint arXiv:1606.06565},
	year = {2016}
}


% Bayesian approximations and samplers
@inproceedings{approx1,
	title = {Weight Uncertainty in Neural Networks},
	author = {Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
	booktitle = {International Conference on Machine Learning},
	year = {2015}
}

@inproceedings{approx2,
	title = {Practical variational inference for neural networks},
	author = {Graves, Alex},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2011}
}

% Jordan Variational Inferece
@inproceedings{jvi,
	title = {An Introduction to Variational Methods for Graphical Models},
	author = {Jordan, Michael I. and Ghahramani, Zoubin and Jaakkola,Tommi S. and Saul, Lawrence K.},
	booktitle = {Machine Learning},
	year = {1999}
}

@inproceedings{whatunc,
	title = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
	author = {Kendall, Alex and Gal, Yarin},
	booktitle = {Conference on Neural Information Processing Systems},
	year = {2017}
}





% Datasets
@techreport{cifar,
	author       = {Krizhevsky, Alex and Hinton, Geoffrey E.}, 
	title        = {Learning Multiple Layers of Features From Tiny Images},
	institution  = {University of Toronto},
	year         = 2009,
	note         = {Technical Report}
}

@article{tim,
	author       = {CS231N}, 
	title        = {Tiny ImageNet},
	journal  = {Stanford University},
	year         = 2017,
	note         = {\url{https://tiny-imagenet.herokuapp.com/}}
}

@inproceedings{svhn,
	title = {Reading Digits in Natural Images with Unsupervised Feature Learning},
	author = {Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y.},
	booktitle = {Neural Information Processing Systems Workshop on Deep Learning and Unsupervised Feature Learning},
	year = 2011,
	note = {\url{http://ufldl.stanford.edu/housenumbers}}
}

@article{lsun,
	title={Lsun: Construction of a Large-scale Image Dataset Using Deep Learning with Humans in the Loop},
	author={Yu, Fisher and Seff, Ari and Zhang, Yinda and Song, Shuran and Funkhouser, Thomas and Xiao, Jianxiong},
	journal={arXiv preprint arXiv:1506.03365},
	year= 2015
}

% Data augmentation
@article{daug-1,
	title={Deep residual learning for image recognition},
	author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	journal={Conference on Computer Vision and Pattern Recognition},
	year= 2016
}

@article{daug-2,
	title={Deep Networks with Stochastic Depth},
	author={Huang, Gao and Sun, Yu and Liu, Zhuang and Sedra, Daniel and Weinberger, Kilian Q.},
	journal={European Conference on Computer Vision},
	year= 2016
}



% Rest
@inproceedings{trust-uncertainty,
	title = {Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift},
	author = {Ovadia, Yaniv and Fertig, Emily and Ren, Jie and Nado, Zachary and Sculley, David and Nowozin, Sebastian and Dillon, Joshua V. and Lakshminarayanan, Balaji and Snoek, Jasper},
	booktitle = {Conference on Neural Information Processing Systems},
	year = 2019
}

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

@book{kevinml,
	author = {Kevin P. Murphy},
	title = {Machine Learning: a Probabilistic Perspective},
	publisher = {MIT Press},
	year = 2012,
	url = "probml.ai"
}

@book{bishopml,
	author = {Christopher M. Bishop},
	title = {Pattern Recognition and Machine Learning},
	publisher = {Springer},
	year = 2018,
}

@inproceedings{decomposition,
	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},
	booktitle = {International Conference on Machine Learning},
	year = 2018
}

@techreport{minka,
	author       = {Minka, Thomas}, 
	title        = {Estimating a Dirichlet Distribution},
	institution  = {Massachusetts Institute of Technology},
	year         = 2000,
	note         = {Technical Report}
}

@inproceedings{revKL,
	title = {Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness},
	author = {Malinin, Andrey and Gales, Mark},
	booktitle = {Conference on Neural Information Processing Systems},
	year = {2019}
}


@inproceedings{spoken-uncertainty,
	title = {Ensemble Approaches for Uncertainty in Spoken Languagege Assessment},
	author = {Wu, X and Knill, K. M and Gales, M. J. F and Malinin, A},
	booktitle = {Interspeech},
	year = {2020}
}

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

@article{wilson,
	title={The case for {B}ayesian deep learning},
	author={Wilson, Andrew Gordon},
	journal={arXiv preprint arXiv:2001.10995},
	year= 2020
}

@inproceedings{cifar-augmentation-1,
	title = {Deep Residual Learning for Image Recognition},
	author = {He, K and Zhang, X and Ren, S and Sun, J},
	booktitle = {CVPR},
	year = {2016}
}

@inproceedings{cifar-augmentation-2,
	title = {Network In Network},
	author = {Lin, Min and Chen, Qiang and Yan, Shuicheng},
	booktitle = {ICLR},
	year = {2014}
}

@inproceedings{auroc-1,
	title = {The Relationship Between Precision-recall and Roc Curves},
	author = {Davis, J and Goadrich, M},
	booktitle = {ICML},
	year = {2006}
}

@inproceedings{auroc-2,
	title = {An Introduction to Roc Analysis},
	author = {Fawcett, Tom},
	booktitle = {Pattern recognition letters},
	year = {2006}
}

@book{deeplearning,
	title={Deep Learning},
	author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	publisher={MIT Press},
	note={\url{http://www.deeplearningbook.org}},
	year={2016}
}

@inproceedings{adam,
	title = {Adam: A Method for Stochastic Optimization},
	author = {Kingma, D. P and Ba, Jimmy},
	booktitle = {ICLR},
	year = {2015}
}


% KD applications
@inproceedings{kd-application1,
	title = {Learning Efficient Object Detection Models with Knowledge Distillation},
	author = {Chen, G and Choi, W and Yu, Y and Han, T and Chandraker, M},
	booktitle = {NeurIPS},
	year = {2017}
}

@inproceedings{kd-application2,
	title = {Knowledge Distillation Across Rnsembles of Multilingual Models for Low-resource Languages},
	author = {Cui, J and Kingsbury, B and Ramabhadran, B and Saon, G and Sercu, T and Audhkhasi, K and Sethy, A and Nussbaum-Thom, M and Rosenberg, A},
	booktitle = {ICASSP},
	year = {2017}
}

@inproceedings{kd-application3,
	title = {Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation},
	author = {Yu, R and Li, A and Morariu, V. I and Davis, L. S},
	booktitle = {ICCV},
	year = {2017}
}

@inproceedings{kd-application4,
	title = {Sequence Student-teacher Training of Deep Neural Networks},
	author = {Wong, J. H. M and Gales, M. J. F},
	booktitle = {Interspeech},
	year = {2016}
}

@inproceedings{kd-application5,
	title = {Domain Adaptation of DNN Acoustic Models using Knowledge Distillation},
	author = {Asami, T and Masumura, R and Yamaguchi, Y and Masataki, H and Aono, Y},
	booktitle = {ICASSP},
	year = {2017}
}

@inproceedings{kendalldriving,
	title = {Learning to Drive in a Day},
	author = {Kendall, Alex and Hawke, Jeffrey and Janz, David and Mazur, Przemyslaw and Reda, Daniele and Allen, John-Mark and Lam, Vinh-Dieu and Bewley, Alex and Shah, Amar},
	booktitle = {International Conference on Robotics and Automation},
	year = {2019}
}

@inproceedings{vadera,
	title = {Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks},
	author = {Vadera, Meet P. and Jalain, Brian and Marlin, Benjamin M.},
	booktitle = {Conference on Uncertainty in Artificial Intelligence},
	year = {2020}
}

@inproceedings{soups,
	title = {Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time},
	author = {Wortsman, Mitchell and Ilharco, Gabriel and Gadre, Samir Yitzhak and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Morcos, Ari S. and Namkoong, Hongseok and Farhadi, Ali and Carmon, Yair and Kornblith, Simon and Schmidt, Ludwig},
	booktitle = {arXiv preprint arXiv:2203.05482},
	year = {2021}
}

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