@inproceedings{molina_2017,
  author    = {Alejandro Molina and
               Sriraam Natarajan and
               Kristian Kersting},
  title     = {Poisson Sum-Product Networks: {A} Deep Architecture for Tractable
               Multivariate Poisson Distributions},
  booktitle = {AAAI},
  year      = {2017}
}


@article{blockeel_1998,
  author    = {Hendrik Blockeel and
               Luc {de Raedt}},
  title     = {Top-Down Induction of First-Order Logical Decision Trees},
  journal   = {AI},
  year      = {1998}
}

@article{dhami_2018,
title = "Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities",
journal = "Smart Health",
year = "2018",
author = "Devendra Singh Dhami and Gautam Kunapuli and Mayukh Das and David Page and Sriraam Natarajan"
}

@inproceedings{poon_2011,
  author    = {Hoifung Poon and
               Pedro M. Domingos},
  title     = {Sum-Product Networks: {A} New Deep Architecture},
  booktitle = {UAI},
  year      = {2011}
}

@inproceedings{molina_2018,
  author    = {Alejandro Molina and
               Antonio Vergari and
               Nicola Di Mauro and
               Sriraam Natarajan and
               Floriana Esposito and
               Kristian Kersting},
  title     = {Mixed Sum-Product Networks: {A} Deep Architecture for Hybrid Domains},
  booktitle = {AAAI},
  year      = {2018}
  }

  @article{kok_2005,
  title={The Alchemy system for statistical relational AI},
  author={Kok, Stanley and Sumner, Marc and Richardson, Matthew and Singla, Parag and Poon, Hoifung and Lowd, Daniel and Wang, Jue and Domingos, Pedro},
  year={2005}
}

@article{mitchell_2018,
  title={Never-ending learning},
  author={Mitchell, Tom and Cohen, William and Hruschka, Estevam and Talukdar, Partha and Yang, Bishan and Betteridge, Justin and Carlson, Andrew and Dalvi, Bhavana and Gardner, Matt and Kisiel, Bryan and others},
  journal={Comm. of the ACM},
  year={2018}
}

@Article{amer_2015,
  Title                    = {Sum Product Networks for Activity Recognition},
  Author                   = {Amer, Mohamed and Todorovic, Sinisa},
  Journal                  = {TPAMI},
  Year                     = {2015},
}

@Article{yuan_2016,
  Title                    = {Modeling spatial layout for scene image understanding via a novel multiscale sum-product network},
  Author                   = {Zehuan Yuan and Hao Wang and Limin Wang and Tong Lu and Shivakumara Palaiahnakote and Chew Lim Tan},
  Journal                  = {Expert Systems with Applications},
  Year = {2016}
}


@InProceedings{cheng_2014,
  Title                    = {Language modeling with {S}um-{P}roduct {N}etworks},
  Author                   = {Wei{-}Chen Cheng and Stanley Kok and Hoai Vu Pham and Hai Leong Chieu and Kian Ming Adam Chai},
  Booktitle                = {INTERSPEECH},
  Year                     = {2014}
}

@InProceedings{gens_2013,
  Title                    = {{Learning the Structure of Sum-Product Networks}},
  Author                   = {Robert Gens and Pedro Domingos},
  Booktitle                = {ICML},
  Year                     = {2013},
}


@InProceedings{peharz_2014,
  Title                    = {Modeling speech with sum-product networks: Application to bandwidth extension},
  Author                   = {Robert Peharz and Georg Kapeller and Pejman Mowlaee and Franz Pernkopf},
  Booktitle                = {ICASSP},
  Year                     = {2014},
}

@Article{ratajczak_2014,
  Title                    = {Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields},
  Author                   = {Ratajczak, Martin and Tschiatschek, S and Pernkopf, F},
  Journal                  = {Proc. of ICML Workshop on Tractable Probabilistic Models},
  year = {2014},
}

@inproceedings{nath_2015,
  title={Learning relational sum-product networks},
  author={Nath, Aniruddh and Domingos, Pedro},
  booktitle={AAAI},
  year={2015}
}

@article{natarajan_2012,
  title={Gradient-based boosting for statistical relational learning: The relational dependency network case},
  author={Natarajan, Sriraam and Khot, Tushar and Kersting, Kristian and Gutmann, Bernd and Shavlik, Jude},
  journal={Machine Learning},
  year={2012}
}

@inproceedings{khot_2011,
  title={Learning Markov logic networks via functional gradient boosting},
  author={Khot, Tushar and Natarajan, Sriraam and Kersting, Kristian and Shavlik, Jude},
  booktitle={ICDM},
  year={2011}
}

@article{richardson_2006,
  title={Markov logic networks},
  author={Richardson, Matthew and Domingos, Pedro},
  journal={Machine learning},
  year={2006}
}

@article{neville_2007,
  title={Relational dependency networks},
  author={Neville, Jennifer and Jensen, David},
  journal={JMLR},
  year={2007}
}

@article{darwiche_2003,
  title={A differential approach to inference in Bayesian networks},
  author={Darwiche, Adnan},
  journal={JACM},
  year={2003}
}

@inproceedings{zhao_2015,
  author    = {Han Zhao and
               Mazen Melibari and
               Pascal Poupart},
  title     = {On the Relationship between Sum-Product Networks and Bayesian Networks},
  booktitle = {ICML},
  year      = {2015},
  timestamp = {Wed, 29 May 2019 08:41:45 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/ZhaoMP15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{flach_2004,
  title={Naive Bayesian classification of structured data},
  author={Flach, Peter A and Lachiche, Nicolas},
  journal={Machine Learning},
  year={2004}
}

@misc{getoor_2007,
  title={Statistical relational learning},
  author={Getoor, Lise and Taskar, Ben},
  year={2007},
  publisher={MIT press Cambridge}
}

@article{raedt_2016,
  title={Statistical relational artificial intelligence: Logic, probability, and computation},
  author={De Raedt, Luc and Kersting, Kristian and Natarajan, Sriraam and Poole, David},
  year={2016},
  publisher={Morgan \& Claypool Publishers}
}

@inproceedings{landwehr_2005,
  title={nFOIL: Integrating na{\i}ve bayes and FOIL},
  author={Landwehr, Niels and Kersting, Kristian and De Raedt, Luc},
  booktitle={AAAI},
  year={2005}
}

@article{dhami_2020,
  title={Non-Parametric Learning of Gaifman Models},
  author={Dhami, Devendra Singh and Yen, Siwen and Kunapuli, Gautam and Natarajan, Sriraam},
  journal={arXiv preprint arXiv:2001.00528},
  year={2020}
}

@article{yang_2013,
  title={On Poisson graphical models},
  author={Yang, Eunho and Ravikumar, Pradeep K and Allen, Genevera I and Liu, Zhandong},
  journal={NIPS},
  year={2013}
}

@article{austin_2020,
title = "Missing Data in Clinical Research: A Tutorial on Multiple Imputation",
journal = "Canadian Journal of Cardiology",
year = "2020",
author = "Peter C. Austin and Ian R. White and Douglas S. Lee and Stef {van Buuren}"
}


@book{little_2019,
  title={Statistical analysis with missing data},
  author={Little, Roderick JA and Rubin, Donald B},
  volume={793},
  year={2019},
  publisher={John Wiley \& Sons}
}

@article{van_buuren_2011,
   author = {Stef van Buuren and Karin Groothuis-Oudshoorn},
   title = {mice: Multivariate Imputation by Chained Equations in R},
   journal = {J. Stat. Softw},
   year = {2011},
}

@article{altman_1992,
 author = {N. S. Altman},
 journal = {The American Statistician},
 title = {An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression},
 year = {1992}
}


@article{vergari_2019,
	Author = {Vergari, Antonio and Di Mauro, Nicola and Esposito, Floriana},
	Journal = {Machine Learning},
	Title = {Visualizing and understanding Sum-Product Networks},
	Year = {2019}
	}


@article{peharz_2017,
  author    = {Robert Peharz and
               Robert Gens and
               Franz Pernkopf and
               Pedro M. Domingos},
  title     = {On the Latent Variable Interpretation in Sum-Product Networks},
  journal   = {TPAMI},
  year      = {2017}
}

@article{papoulis_2002,
author = {Papoulis, Athanasios and Pillai, Unnikrishna},
year = {2002},
title = {Probability, Random Variables, and Stochastic Processes, Fourth Edition}
}

@article{vapnik_2009,
author = {Vapnik, Vladimir and Vashist, Akshay},
title = {A New Learning Paradigm: Learning Using Privileged Information},
year = {2009},
issue_date = {July, 2009},
publisher = {Elsevier Science Ltd.},
address = {GBR},
volume = {},
number = {},
issn = {0893-6080},
abstract = {In the Afterword to the second edition of the book ''Estimation of Dependences Based on Empirical Data'' by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an extension of the SVM method (the so called SVM"@c+ method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas.},
journal = {Neural Networks},
month = jul,
pages = {},
numpages = {14},
keywords = {Privileged information, Hidden information, Oracle SVM, Machine learning, SVM, SVM+, Learning with teacher}
}

@article{darwiche_2002,
  author    = {Adnan Darwiche and
               Pierre Marquis},
  title     = {A Knowledge Compilation Map},
  journal   = {JAIR},
  volume    = {},
  pages     = {},
  year      = {2002},
  timestamp = {Mon, 21 Jan 2019 15:01:18 +0100},
  biburl    = {https://dblp.org/rec/journals/jair/DarwicheM02.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{peharz_2015,
title = {On Theoretical Properties of Sum-Product Networks},
author = {Robert Peharz and Sebastian Tschiatschek and Franz Pernkopf and Pedro Domingos},
booktitle = {AISTATS},
year = {2015}
}

@article{cawley_2010,
  title={On over-fitting in model selection and subsequent selection bias in performance evaluation},
  author={Cawley, Gavin C and Talbot, Nicola LC},
  journal={JMLR},
  year={2010}
}

@inproceedings{kirichenko_2020,
  title={Why normalizing flows fail to detect out-of-distribution data},
  author={Kirichenko, Polina and Izmailov, Pavel and Wilson, Andrew Gordon},
  booktitle={NeurIPS},
  year={2020}
}

@inproceedings{tan_2019,
  author    = {Ming Tan and
               Yang Yu and
               Haoyu Wang and
               Dakuo Wang and
               Saloni Potdar and
               Shiyu Chang and
               Mo Yu},
  title     = {Out-of-Domain Detection for Low-Resource Text Classification Tasks},
  booktitle = {EMNLP-IJCNLP},
  year      = {2019}
}

@article{zheng_2020,
  author    = {Yinhe Zheng and
               Guanyi Chen and
               Minlie Huang},
  title     = {Out-of-Domain Detection for Natural Language Understanding in Dialog
               Systems},
  journal   = {{IEEE} {ACM} Trans. Audio Speech Lang. Process.},
  year      = {2020}
}

@inproceedings{lee_2018,
  author    = {Kimin Lee and
               Kibok Lee and
               Honglak Lee and
               Jinwoo Shin},
  title     = {A Simple Unified Framework for Detecting Out-of-Distribution Samples
               and Adversarial Attacks},
  booktitle = {NeurIPS},
  year      = {2018}
}

@inproceedings{peharz_2019,
  author    = {Robert Peharz and
               Antonio Vergari and
               Karl Stelzner and
               Alejandro Molina and
               Martin Trapp and
               Xiaoting Shao and
               Kristian Kersting and
               Zoubin Ghahramani},
  title     = {Random Sum-Product Networks: {A} Simple and Effective Approach to
               Probabilistic Deep Learning},
  booktitle = {UAI},
  year      = {2019}
}


@InProceedings{peharz_2020,
  title = 	 {Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning},
  author =       {Peharz, Robert and Vergari, Antonio and Stelzner, Karl and Molina, Alejandro and Shao, Xiaoting and Trapp, Martin and Kersting, Kristian and Ghahramani, Zoubin},
  booktitle = 	 {UAI},
  year = 	 {2020},
  abstract = 	 {Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact and efficient inference routines. However, in order to guarantee exact inference, they require specific structural constraints, which complicate learning SPNs from data. Thereby, most SPN structure learners proposed so far are tedious to tune, do not scale easily, and are not easily integrated with deep learning frameworks. In this paper, we follow a simple “deep learning” approach, by generating unspecialized random structures, scalable to millions of parameters, and subsequently applying GPU-based optimization. Somewhat surprisingly, our models often perform on par with state-of-the-art SPN structure learners and deep neural networks on a diverse range of generative and discriminative scenarios. At the same time, our models yield well-calibrated uncertainties, and stand out among most deep generative and discriminative models in being robust to missing features and being able to detect anomalies.}
}


@inproceedings{chattopadhyay_2020,
  author    = {Prithvijit Chattopadhyay and
               Yogesh Balaji and
               Judy Hoffman},
  title     = {Learning to Balance Specificity and Invariance for In and Out of Domain
               Generalization},
  booktitle = {ECCV},
  year      = {2020}
}

@inproceedings{gangal_2020,
  author    = {Varun Gangal and
               Abhinav Arora and
               Arash Einolghozati and
               Sonal Gupta},
  title     = {Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain
               Detection in Task Oriented Dialog},
  booktitle = {AAAI},
  year      = {2020}
}

@article{landwehr_2007,
  title={Integrating naive bayes and FOIL.},
  author={Landwehr, Niels and Kersting, Kristine and De Raedt, Luc},
  journal={JMLR},
  year={2007}
}

@inproceedings{tran_2019,
  author    = {Dustin Tran and
               Keyon Vafa and
               Kumar Krishna Agrawal and
               Laurent Dinh and
               Ben Poole},
  title     = {Discrete Flows: Invertible Generative Models of Discrete Data},
  booktitle = {NeurIPS 2019},
  year      = {2019}
}

@techreport{choi_2020,
  title={Probabilistic circuits: A unifying framework for tractable probabilistic models},
  author={Choi, YooJung and Vergari, Antonio and Van den Broeck, Guy},
  year={2020},
  institution={UCLA}
}

@inproceedings{stelzner_2019,
author = {Stelzner, Karl and Peharz, Robert and Kersting, Kristian},
title = {Faster Attend-Infer-Repeat with Tractable Probabilistic Models},
booktitle = {{ICML}},
year = {2019}
}

@inproceedings{butz_2019,
author = {Butz, Cory J. and de S. Oliveira, Jhonatan and dos Santos, Andr{'{e}} E. and Teixeira, Andr{'{e}} L.},
title = {Deep Convolutional Sum-Product Networks},
booktitle = {{AAAI}},
year = {2019}
}

@inproceedings{amer_2012,
title={Sum-product networks for modeling activities with stochastic structure},
author={Amer, Mohamed and Todorovic, Sinisa},
booktitle={CVPR},
year={2012},
}

@inproceedings{trapp_2020,
author = {Trapp, Martin and Peharz, Robert and Pernkopf, Franz and Rasmussen, Carl E.},
title = {Deep Structured Mixtures of Gaussian Processes},
booktitle = {AISTATS},
year = {2020}
}

@inproceedings{zheng_2018,
author = {Zheng, Kaiyu and Pronobis, Andrzej and Rao, Rajesh P. N.},
title = {Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic
Maps},
booktitle = {{AAAI}},
year = {2018}
}

@inproceedings{yu_2021_icml,
  author    = {Zhongjie Yu and
               Fabrizio G. Ventola and
               Kristian Kersting},
  title     = {Whittle Networks: {A} Deep Likelihood Model for Time Series},
  booktitle = {ICML},
  year      = {2021},
  timestamp = {Wed, 25 Aug 2021 17:11:17 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/0001VK21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{yu_2021_uai,
  author    = {Zhongjie Yu and
               Mingye Zhu and
               Martin Trapp and
               Arseny Skryagin and
               Kristian Kersting},
  title     = {Leveraging probabilistic circuits for nonparametric multi-output regression},
  booktitle = {UAI},
  year      = {2021},
  timestamp = {Mon, 20 Dec 2021 08:31:01 +0100},
  biburl    = {https://dblp.org/rec/conf/uai/YuZTSK21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{yu_2022_uai, 
  author    = {Zhongjie Yu and 
               Fabrizio Ventola and 
               Nils Thoma and 
               Devendra Singh Dhami and 
               Martin Mundt and 
               Kristian Kersting},
  title     = {Predictive Whittle Networks for Time Series},
  booktitle = {UAI}, 
  year      = {2022},
}

@inproceedings{correia_2020,
  author    = {Alvaro H. C. Correia and
               Robert Peharz and
               Cassio P. de Campos},
  title     = {Joints in Random Forests},
  booktitle = {NeurIPS},
  year      = {2020},
  timestamp = {Tue, 19 Jan 2021 15:57:47 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/CorreiaPC20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{khosravi_2020,
author = {Khosravi, Pasha and Vergari, Antonio and Choi, YooJung and Liang, Yitao and Van den Broeck, Guy},
title = {Handling Missing Data in Decision Trees: {A} Probabilistic Approach},
journal = {arXiv preprint arXiv:2006.16341},
year = {2020}
}

@inproceedings{nguyen_2015,
  author    = {Anh Mai Nguyen and
               Jason Yosinski and
               Jeff Clune},
  title     = {Deep neural networks are easily fooled: High confidence predictions
               for unrecognizable images},
  booktitle = {CVPR},
  year      = {2015},
  timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
  biburl    = {https://dblp.org/rec/conf/cvpr/NguyenYC15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{amodei_2016,
  author    = {Dario Amodei and
               Chris Olah and
               Jacob Steinhardt and
               Paul F. Christiano and
               John Schulman and
               Dan Man{\'{e}}},
  title     = {Concrete Problems in {AI} Safety},
  journal   = {arXiv preprint arXiv:1606.06565},
  year      = {2016},
  eprinttype = {arXiv},
  eprint    = {1606.06565}
}

@inproceedings{guo_2017,
  author    = {Chuan Guo and
               Geoff Pleiss and
               Yu Sun and
               Kilian Q. Weinberger},
  title     = {On Calibration of Modern Neural Networks},
  booktitle = {ICML},
  year      = {2017},
  timestamp = {Wed, 29 May 2019 08:41:45 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/GuoPSW17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{gal_2016,
  author    = {Yarin Gal and
               Zoubin Ghahramani},
  title     = {Dropout as a Bayesian Approximation: Representing Model Uncertainty
               in Deep Learning},
  booktitle = {ICML},
  year      = {2016},
  timestamp = {Wed, 29 May 2019 08:41:46 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/GalG16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{srivastava_2014,
  author  = {Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
  title   = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
  journal = {JMLR},
  year    = {2014}
}

@book{neal_2012,
  title={Bayesian learning for neural networks},
  author={Neal, Radford M},
  year={2012},
  publisher={Springer Science \& Business Media}
}

@article{mackay_1992,
  title={A practical Bayesian framework for backpropagation networks},
  author={MacKay, David JC},
  journal={Neural computation},
  year={1992},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@article{hansen_1990,
  author    = {Lars Kai Hansen and
               Peter Salamon},
  title     = {Neural Network Ensembles},
  journal   = {TPAMI},
  year      = {1990},
  timestamp = {Fri, 09 Apr 2021 18:35:08 +0200},
  biburl    = {https://dblp.org/rec/journals/pami/HansenS90.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{lakshminarayanan_2017,
  author    = {Balaji Lakshminarayanan and
               Alexander Pritzel and
               Charles Blundell},
  title     = {Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles},
  booktitle = {NeurIPS},
  year      = {2017},
  timestamp = {Thu, 21 Jan 2021 15:15:21 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/Lakshminarayanan17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{osband_2021,
  author    = {Ian Osband and
               Zheng Wen and
               Mohammad Asghari and
               Morteza Ibrahimi and
               Xiyuan Lu and
               Benjamin Van Roy},
  title     = {Epistemic Neural Networks},
  journal   = {arXiv preprint arXiv:2107.08924},
  year      = {2021},
  eprinttype = {arXiv},
  eprint    = {2107.08924},
  timestamp = {Thu, 22 Jul 2021 11:14:11 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2107-08924.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{oala_2021,
  author    = {Luis Oala and
               Cosmas Hei{\ss} and
               Jan MacDonald and
               Maximilian M{\"{a}}rz and
               Gitta Kutyniok and
               Wojciech Samek},
  title     = {Detecting failure modes in image reconstructions with interval neural
               network uncertainty},
  journal={International Journal of Computer Assisted Radiology and Surgery},
  year      = {2021}
}

@inproceedings{antoran_2020,
  author    = {Javier Antor{\'{a}}n and
               James Urquhart Allingham and
               Jos{\'{e}} Miguel Hern{\'{a}}ndez{-}Lobato},
  title     = {Depth Uncertainty in Neural Networks},
  booktitle = {NeurIPS},
  year      = {2020},
  timestamp = {Tue, 19 Jan 2021 15:57:24 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/AntoranAH20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{louizos_2016,
  author    = {Christos Louizos and
               Max Welling},
  title     = {Structured and Efficient Variational Deep Learning with Matrix Gaussian
               Posteriors},
  booktitle = {ICML},
  year      = {2016},
  timestamp = {Wed, 29 May 2019 08:41:46 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/LouizosW16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{graves_2011,
  author    = {Alex Graves},
  title     = {Practical Variational Inference for Neural Networks},
  booktitle = {NIPS},
  year      = {2011},
  timestamp = {Thu, 21 Jan 2021 15:15:24 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/Graves11.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{blundell_2015,
  author    = {Charles Blundell and
               Julien Cornebise and
               Koray Kavukcuoglu and
               Daan Wierstra},
  title     = {Weight Uncertainty in Neural Network},
  booktitle = {ICML},
  year      = {2015}
}

@article{liu_2020,
  title={When Gaussian Process Meets Big Data: A Review of Scalable GPs},
  author={Haitao Liu and Y. Ong and Xiaobo Shen and Jianfei Cai},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2020},
}

@inproceedings{daxberger_2021,
  author    = {Erik A. Daxberger and
               Eric T. Nalisnick and
               James Urquhart Allingham and
               Javier Antor{\'{a}}n and
               Jos{\'{e}} Miguel Hern{\'{a}}ndez{-}Lobato},
  title     = {Bayesian Deep Learning via Subnetwork Inference},
  booktitle = {ICML},
  year      = {2021},
  timestamp = {Wed, 25 Aug 2021 17:11:17 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/DaxbergerNAAH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{nalisnick_2019,
  author    = {Eric T. Nalisnick and
               Akihiro Matsukawa and
               Yee Whye Teh and
               Dilan G{\"{o}}r{\"{u}}r and
               Balaji Lakshminarayanan},
  title     = {Do Deep Generative Models Know What They Don't Know?},
  booktitle = {ICLR},
  year      = {2019}
}

@inproceedings{peharz_2020_icml,
  author    = {Robert Peharz and
               Steven Lang and
               Antonio Vergari and
               Karl Stelzner and
               Alejandro Molina and
               Martin Trapp and
               Guy Van den Broeck and
               Kristian Kersting and
               Zoubin Ghahramani},
  title     = {Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic
               Circuits},
  booktitle = {ICML},
  year      = {2020},
  timestamp = {Tue, 15 Dec 2020 17:40:19 +0100},
  biburl    = {https://dblp.org/rec/conf/icml/PeharzLVS00BKG20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}




@InProceedings{izmailov_2021,
  title = 	 {What Are Bayesian Neural Network Posteriors Really Like?},
  author =       {Izmailov, Pavel and Vikram, Sharad and Hoffman, Matthew D and Wilson, Andrew Gordon Gordon},
  booktitle = 	 {ICML},
  year = 	 {2021},
  abstract = 	 {The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex. For computational reasons, researchers approximate this posterior using inexpensive mini-batch methods such as mean-field variational inference or stochastic-gradient Markov chain Monte Carlo (SGMCMC). To investigate foundational questions in Bayesian deep learning, we instead use full batch Hamiltonian Monte Carlo (HMC) on modern architectures. We show that (1) BNNs can achieve significant performance gains over standard training and deep ensembles; (2) a single long HMC chain can provide a comparable representation of the posterior to multiple shorter chains; (3) in contrast to recent studies, we find posterior tempering is not needed for near-optimal performance, with little evidence for a “cold posterior” effect, which we show is largely an artifact of data augmentation; (4) BMA performance is robust to the choice of prior scale, and relatively similar for diagonal Gaussian, mixture of Gaussian, and logistic priors; (5) Bayesian neural networks show surprisingly poor generalization under domain shift; (6) while cheaper alternatives such as deep ensembles and SGMCMC can provide good generalization, their predictive distributions are distinct from HMC. Notably, deep ensemble predictive distributions are similarly close to HMC as standard SGLD, and closer than standard variational inference.}
}

@article{molina_2019,
  author    = {Alejandro Molina and
               Antonio Vergari and
               Karl Stelzner and
               Robert Peharz and
               Pranav Subramani and
               Nicola Di Mauro and
               Pascal Poupart and
               Kristian Kersting},
  title     = {SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning
               using Sum-Product Networks},
  journal={arXiv preprint arXiv:1901.03704},
  year      = {2019},
  eprinttype = {arXiv},
  eprint    = {1901.03704},
  timestamp = {Fri, 01 Feb 2019 13:39:59 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1901-03704.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{bradshaw_2017,
  title={Adversarial examples, uncertainty, and transfer testing robustness in Gaussian process hybrid deep networks},
  author={Bradshaw, John and Matthews, Alexander G de G and Ghahramani, Zoubin},
  journal={arXiv preprint arXiv:1707.02476},
  year={2017}
}

@article{lecun_1998,
  author    = {Yann LeCun and
               L{\'{e}}on Bottou and
               Yoshua Bengio and
               Patrick Haffner},
  title     = {Gradient-based learning applied to document recognition},
  journal   = {Proc. {IEEE}},
  year      = {1998},
  timestamp = {Wed, 16 Mar 2022 23:54:13 +0100},
  biburl    = {https://dblp.org/rec/journals/pieee/LeCunBBH98.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{xiao_2017,
  author    = {Han Xiao and
               Kashif Rasul and
               Roland Vollgraf},
  title     = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
               Algorithms},
  journal={arXiv preprint arXiv:1708.07747},
  year      = {2017},
  eprinttype = {arXiv},
  eprint    = {1708.07747},
  timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1708-07747.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{mu_2019,
  author    = {Norman Mu and
               Justin Gilmer},
  title     = {{MNIST-C:} {A} Robustness Benchmark for Computer Vision},
  journal={arXiv preprint arXiv:1906.02337},
  year      = {2019},
  eprinttype = {arXiv},
  eprint    = {1906.02337},
  timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-02337.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}



@inproceedings{ovadia_2019,
  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 and Lakshminarayanan, Balaji and Snoek, Jasper},
  booktitle ={NeurIPS},
  year={2019}
}


@article{hilprecht_2020,
  author    = {Benjamin Hilprecht and
               Andreas Schmidt and
               Moritz Kulessa and
               Alejandro Molina and
               Kristian Kersting and
               Carsten Binnig},
  title     = {DeepDB: Learn from Data, not from Queries!},
  journal   = {Proc. {VLDB} Endow.},
  volume    = {13},
  number    = {7},
  pages     = {992--1005},
  year      = {2020},
  timestamp = {Fri, 09 Apr 2021 18:29:50 +0200},
  biburl    = {https://dblp.org/rec/journals/pvldb/HilprechtSKMKB20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{nath_2016,
  author    = {Aniruddh Nath and
               Pedro M. Domingos},
  title     = {Learning Tractable Probabilistic Models for Fault Localization},
  booktitle = {Proceedings of the Thirtieth {AAAI} Conference on Artificial Intelligence,
               February 12-17, 2016, Phoenix, Arizona, {USA}},
  pages     = {1294--1301},
  publisher = {{AAAI} Press},
  year      = {2016},
  url       = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12350},
  timestamp = {Wed, 10 Feb 2021 08:44:59 +0100},
  biburl    = {https://dblp.org/rec/conf/aaai/NathD16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{zheng_2019,
  author    = {Kaiyu Zheng and
               Andrzej Pronobis},
  title     = {From Pixels to Buildings: End-to-end Probabilistic Deep Networks for
               Large-scale Semantic Mapping},
  booktitle = {IROS},
  year      = {2019},
  timestamp = {Fri, 31 Jan 2020 13:28:16 +0100},
  biburl    = {https://dblp.org/rec/conf/iros/ZhengP19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{delalleau_2011,
  author    = {Olivier Delalleau and
               Yoshua Bengio},
  title     = {Shallow vs. Deep Sum-Product Networks},
  booktitle = {NIPS},
  year      = {2011},
  timestamp = {Mon, 16 May 2022 15:41:51 +0200},
  biburl    = {https://dblp.org/rec/conf/nips/DelalleauB11.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{postels_2019,
  title={Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation},
  author={Janis Postels and Francesco Ferroni and Huseyin Coskun and Nassir Navab and Federico Tombari},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages={2931-2940}
}

@InProceedings{rooshenas_2014,
  title = 	 {Learning Sum-Product Networks with Direct and Indirect Variable Interactions},
  author = 	 {Rooshenas, Amirmohammad and Lowd, Daniel},
  booktitle = 	 {ICML},
  year = 	 {2014}
}

@inproceedings{adel_2015,
  title={Learning the Structure of Sum-Product Networks via an SVD-based Algorithm},
  author={Tameem Adel and David Balduzzi and Ali Ghodsi},
  booktitle={UAI},
  year={2015}
}

@article{hullermeier_2021,
  author    = {Eyke H{\"{u}}llermeier and
               Willem Waegeman},
  title     = {Aleatoric and epistemic uncertainty in machine learning: an introduction
               to concepts and methods},
  journal   = {Machine Learning},
  year      = {2021}
}

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

@article{cinic,
  author    = {Luke Nicholas Darlow and
               Elliot J. Crowley and
               Antreas Antoniou and
               Amos J. Storkey},
  title     = {{CINIC-10} is not ImageNet or {CIFAR-10}},
  journal={arXiv preprint arXiv:1810.03505},
  year      = {2018}
}

@inproceedings{svhn,
    author = {Yuval Netzer and Tao Wang and Adam Coates and Alessandro Bissacco and Bo Wu and Andrew Y. Ng},
    title = {Reading Digits in Natural Images with Unsupervised Feature Learning},
    booktitle = {NIPS Workshop on Deep Learning and Unsupervised Feature Learning},
    year = {2011}
}
    
@inproceedings{emnist,
  author    = {Fatemeh Alizadeh and
               Aikaterini Mniestri and
               Alarith Uhde and
               Gunnar Stevens},
  editor    = {Simone D. J. Barbosa and
               Cliff Lampe and
               Caroline Appert and
               David A. Shamma},
  title     = {On Appropriation and Nostalgic Reminiscence of Technology},
  booktitle = {{CHI} '22: {CHI} Conference on Human Factors in Computing Systems,
               New Orleans, LA, USA, 29 April 2022 - 5 May 2022, Extended Abstracts},
  pages     = {287:1--287:6},
  publisher = {{ACM}},
  year      = {2022},
  url       = {https://doi.org/10.1145/3491101.3519676},
  doi       = {10.1145/3491101.3519676},
  timestamp = {Wed, 04 May 2022 13:02:17 +0200},
  biburl    = {https://dblp.org/rec/conf/chi/AlizadehMUS22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{kmnist,
  author    = {Tarin Clanuwat and
               Mikel Bober{-}Irizar and
               Asanobu Kitamoto and
               Alex Lamb and
               Kazuaki Yamamoto and
               David Ha},
  title     = {Deep Learning for Classical Japanese Literature},
  journal   = {CoRR},
  volume    = {abs/1812.01718},
  year      = {2018},
  url       = {http://arxiv.org/abs/1812.01718},
  eprinttype = {arXiv},
  eprint    = {1812.01718},
  timestamp = {Thu, 14 Oct 2021 09:15:14 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1812-01718.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@techreport{cifar100,
  title={Learning Multiple Layers of Features from Tiny Images},
  author={Krizhevsky, Alex},
  year={2009},
  institution={U. of Toronto}
}

@article{cifar100_old,
title= {CIFAR-100 (Canadian Institute for Advanced Research)},
journal= {},
author= {Alex Krizhevsky and Vinod Nair and Geoffrey Hinton},
year= {},
url= {http://www.cs.toronto.edu/~kriz/cifar.html},
abstract= {This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).
Here is the list of classes in the CIFAR-100:

Superclass	Classes
aquatic mammals	beaver, dolphin, otter, seal, whale
fish	aquarium fish, flatfish, ray, shark, trout
flowers	orchids, poppies, roses, sunflowers, tulips
food containers	bottles, bowls, cans, cups, plates
fruit and vegetables	apples, mushrooms, oranges, pears, sweet peppers
household electrical devices	clock, computer keyboard, lamp, telephone, television
household furniture	bed, chair, couch, table, wardrobe
insects	bee, beetle, butterfly, caterpillar, cockroach
large carnivores	bear, leopard, lion, tiger, wolf
large man-made outdoor things	bridge, castle, house, road, skyscraper
large natural outdoor scenes	cloud, forest, mountain, plain, sea
large omnivores and herbivores	camel, cattle, chimpanzee, elephant, kangaroo
medium-sized mammals	fox, porcupine, possum, raccoon, skunk
non-insect invertebrates	crab, lobster, snail, spider, worm
people	baby, boy, girl, man, woman
reptiles	crocodile, dinosaur, lizard, snake, turtle
small mammals	hamster, mouse, rabbit, shrew, squirrel
trees	maple, oak, palm, pine, willow
vehicles 1	bicycle, bus, motorcycle, pickup truck, train
vehicles 2	lawn-mower, rocket, streetcar, tank, tractor

Yes, I know mushrooms aren't really fruit or vegetables and bears aren't really carnivores. },
keywords= {Dataset},
terms= {}
}

@inproceedings{hendrycks_2019,
  author    = {Dan Hendrycks and
               Thomas G. Dietterich},
  title     = {Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  booktitle = {ICLR},
  year      = {2019}
}

@article{michaelis_2019,
  title={Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming},
  author={Michaelis, Claudio and Mitzkus, Benjamin and 
    Geirhos, Robert and Rusak, Evgenia and 
    Bringmann, Oliver and Ecker, Alexander S. and 
    Bethge, Matthias and Brendel, Wieland},
  journal={arXiv preprint arXiv:1907.07484},
  year={2019}
}

@techreport{ratio_seltman,
  title = {Approximations for Mean and Variance of a Ratio},
  howpublished = {\url{https://www.stat.cmu.edu/~hseltman/files/ratio.pdf}},
  author = {Howard Seltman},
  year = {2018},
  institution={CMU}
}

@inproceedings{matan_90, 
    title={Handwritten Character Recognition Using Neural Network Architectures}, 
    author={Matan, Ofer and Kiang, RK and Stenard, CE and Boser, B and Denker, JS and Henderson, Don and Howard, RE and Hubbard, W and Jackel, LD and Le Cun, Yann},
    booktitle={USPS advanced technology conference},
    year = {1990}
}

@article{scheirer_2013,
  title={Toward open set recognition},
  author={Scheirer, Walter J and de Rezende Rocha, Anderson and Sapkota, Archana and Boult, Terrance E},
  journal={TPAMI},
  year={2013}
}

@article{scheirer_2014,
  author={Scheirer, Walter J. and Jain, Lalit P. and Boult, Terrance E.},
  journal={TPAMI}, 
  title={Probability Models for Open Set Recognition}, 
  year={2014}
}


@inproceedings{ahn_2019,
    title = {Uncertainty-based Continual Learning with Adaptive Regularization},
    author={Ahn, Hongjoon and Cha, Sungmin and Lee, Donggyu and Moon, Taesup},
    booktitle = {NeurIPS},
    year = {2019}
}


@inproceedings{ebrahimi_2020,
    title = {Uncertainty-guided Continual Learning with Bayesian Neural Networks},
    author={Ebrahimi, Sayna and Elhoseiny, Mohamed and Darrell, Trevor and Rohrbach, Marcus},
    booktitle = {ICLR},
    year = {2020}
}

@inproceedings{gal_2017,
    title = {Deep Bayesian Active Learning with Image Data},
    author={Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
    booktitle = {ICML},
    year = {2017}
}

@inproceedings{hendrycks_2017,
    title = {A baseline for detecting misclassified and out-of-distribution examples in neural networks},
    author = {Hendrycks, Dan and Gimpel, Kevin},
    booktitle = {ICLR},
    year = {2017}
}

@article{bartlett_2008,
    title = {Classification with a reject option using a hinge loss},
    author={Bartlett, Peter L and Wegkamp, Marten H},
    journal = {Journal of Machine Learning Research},
    year = {2008}
}

@inproceedings{boult_2019,
    title = {Learning and the Unknown: Surveying Steps Toward Open World Recognition},
    author={Boult, Terrance E. and Cruz, Steve and Dhamija, Akshay Raj and Gunther, Manuel and Henrydoss, James and Scheirer, Walter J.},
    booktitle={AAAI},
    year = {2019}
}

@inproceedings{miller_2018,
  title={Dropout sampling for robust object detection in open-set conditions},
  author={Miller, Dimity and Nicholson, Lachlan and Dayoub, Feras and S{\"u}nderhauf, Niko},
  booktitle={ICRA},
  year={2018}
}

@article{mundt_2022,
  title={Unified probabilistic deep continual learning through generative replay and open set recognition},
  author={Mundt, Martin and Pliushch, Iuliia and Majumder, Sagnik and Hong, Yongwon and Ramesh, Visvanathan},
  journal={Journal of Imaging},
  year={2022}
}

@inproceedings{yuq_2019,
  title={Unsupervised out-of-distribution detection by maximum classifier discrepancy},
  author={Yu, Qing and Aizawa, Kiyoharu},
  booktitle={ICCV},
  year={2019}
}

@inproceedings{kendall_2017,
    title = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
    author = {Kendall, Alex and Gal, Yarin},
    booktitle = {NeurIPS},
    year = {2017}
}

@inproceedings{kendall2017a,
    title = {Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding},
    author = {Kendall, Alex and Badrinarayanan, Vijay and Cipolla, Roberto},
    booktitle = {BMVC},
    year = {2017}
}

@inproceedings{hernandez_lobato_2015,
  author    = {Jos{\'{e}} Miguel Hern{\'{a}}ndez{-}Lobato and
               Ryan P. Adams},
  title     = {Probabilistic Backpropagation for Scalable Learning of Bayesian Neural
               Networks},
  booktitle = {ICML},
  year      = {2015}
}

@inproceedings{mishkin_2018,
  author    = {Aaron Mishkin and
               Frederik Kunstner and
               Didrik Nielsen and
               Mark Schmidt and
               Mohammad Emtiyaz Khan},
  title     = {{SLANG:} Fast Structured Covariance Approximations for Bayesian Deep
               Learning with Natural Gradient},
  booktitle = {NeurIPS},
  year      = {2018}
}

@inproceedings{maddox_2019,
  author    = {Wesley J. Maddox and
               Pavel Izmailov and
               Timur Garipov and
               Dmitry P. Vetrov and
               Andrew Gordon Wilson},
  title     = {A Simple Baseline for Bayesian Uncertainty in Deep Learning},
  booktitle = {NeurIPS},
  year      = {2019}
}

@inproceedings{khan_2019,
  author    = {Mohammad Emtiyaz Khan and
               Alexander Immer and
               Ehsan Abedi and
               Maciej Korzepa},
  title     = {Approximate Inference Turns Deep Networks into Gaussian Processes},
  booktitle = {NeurIPS},
  year      = {2019}
}

@inproceedings{kingma2014auto,
  title={Auto-encoding variational Bayes},
  author={Kingma, Diederik P. and Welling, Max},
  booktitle={ICLR},
  year      = {2014}
}

@article{kobyzev2020normalizing,
  title={Normalizing flows: An introduction and review of current methods},
  author={Kobyzev, Ivan and Prince, Simon JD and Brubaker, Marcus A},
  journal={TPAMI},
  year={2020}
}

@article{papamakarios2021normalizing,
  title={Normalizing Flows for Probabilistic Modeling and Inference},
  author={Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji},
  journal={JMLR},
  year={2021}
}

@inproceedings{kingma2015adam,
  title={Adam: A Method for Stochastic Optimization},
  author={Kingma, Diederik P. and Ba, Jimmy},
  booktitle={ICLR},
  year={2015}
}

@inproceedings{vergari_2015,
  author    = {Antonio Vergari and
               Nicola Di Mauro and
               Floriana Esposito},
  title     = {Simplifying, Regularizing and Strengthening Sum-Product Network Structure
               Learning},
  booktitle = {ECML / PKDD},
  year      = {2015},
}

@article{cerutti_2022,
  author    = {Federico Cerutti and
               Lance M. Kaplan and
               Angelika Kimmig and
               Murat Sensoy},
  title     = {Handling epistemic and aleatory uncertainties in probabilistic circuits},
  journal   = {Machine Learning},
  year      = {2022},
}


@InProceedings{peddi_2022,
  title = 	 {Robust learning of tractable probabilistic models},
  author =       {Peddi, Rohith and Rahman, Tahrima and Gogate, Vibhav},
  booktitle = 	 {UAI},
  year = 	 {2022},
}

@article{maua_2017,
    title = {Robustifying sum-product networks},
    journal = {International Journal of Approximate Reasoning},
    year = {2018},
    author = {Denis {Deratani Mauá} and Diarmaid Conaty and Fabio {Gagliardi Cozman} and Katja Poppenhaeger and Cassio {Polpo de Campos}},
}
@inproceedings{liang2019logistic-circuits,
  author       = {Yitao Liang and
                  Guy {Van den Broeck}},
  title        = {Learning Logistic Circuits},
  booktitle    = {AAAI},
  year         = {2019},
}