

@book{miller2006applied,
  title={Applied asymptotic analysis},
  author={Miller, Peter David},
  volume={75},
  year={2006},
  publisher={American Mathematical Soc.}
}

@article{lapinski2019,
title = {Multivariate {L}aplace’s approximation with estimated error and application to limit theorems},
journal = {Journal of Approximation Theory},
volume = {248},
pages = {105305},
year = {2019},
issn = {0021-9045},
doi = {https://doi.org/10.1016/j.jat.2019.105305},
url = {https://www.sciencedirect.com/science/article/pii/S0021904519301108},
author = {Tomasz M. {\L}api{\'n}ski},
}



@article{einstein,
author =       "Albert Einstein",
title =        "{Zur Elektrodynamik bewegter Körper}",
journal =      "Annalen der Physik",
volume =       "322",
number =       "10",
pages =        "891--921",
year =         "1905"}

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

@book{konishi2008information,
  title={Information criteria and statistical modeling},
  author={Konishi, Sadanori and Kitagawa, Genshiro},
  year = {2008},
  publisher={Springer}
}

@article{schwarz1978estimating,
  title={Estimating the Dimension of a Model},
  author={Schwarz, Gideon},
  journal={The Annals of Statistics},
  volume={6},
  number={2},
  pages={461--464},
  year={1978}
}

@inproceedings{pise2008survey,
  title={A survey of semi-supervised learning methods},
  author={Pise, Nitin Namdeo and Kulkarni, Parag},
  booktitle={2008 International conference on computational intelligence and security},
  volume={2},
  pages={30--34},
  year={2008},
  organization={IEEE}
}
@article{van2020survey,
  title={A survey on semi-supervised learning},
  author={Van Engelen, Jesper E and Hoos, Holger H},
  journal={Machine Learning},
  volume={109},
  number={2},
  pages={373--440},
  year={2020},
  publisher={Springer}
}

@book{walley1991statistical,
  title={Statistical reasoning with imprecise probabilities},
  author={Walley, Peter},
  year={1991},
  publisher={Chapman \& Hall}
}

@inproceedings{
zhang2022unifying,
title={Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond},
author={Dinghuai Zhang and Jie Fu and Yoshua Bengio and Aaron Courville},
booktitle={International Conference on Learning Representations, 2022},
year={2022},
url={https://openreview.net/forum?id=1HxTO6CTkz}
}


@InProceedings{pmlr-v97-greenberg19a,
  title = 	 {Automatic Posterior Transformation for Likelihood-Free Inference},
  author =       {Greenberg, David and Nonnenmacher, Marcel and Macke, Jakob},
  booktitle = 	 {36th International Conference on Machine Learning},
  pages = 	 {2404--2414},
  year = 	 {2019},
  volume = 	 {97},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/greenberg19a/greenberg19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/greenberg19a.html},
  abstract = 	 {How can one perform {B}ayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators. However, existing methods are limited to a narrow range of proposal distributions or require importance weighting that can limit performance in practice. Here we present automatic posterior transformation (APT), a new sequential neural posterior estimation method for simulation-based inference. APT can modify the posterior estimate using arbitrary, dynamically updated proposals, and is compatible with powerful flow-based density estimators. It is more flexible, scalable and efficient than previous simulation-based inference techniques. APT can operate directly on high-dimensional time series and image data, opening up new applications for likelihood-free inference.}
}

@article{gutmann2016bayesian,
  title={{B}ayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models},
  author={Gutmann, Michael U and Corander, Jukka},
  journal={Journal of Machine Learning Research},
  volume={17},
  pages={1--47},
  year={2016}
}


@book{barber2012bayesian,
  title={{B}ayesian reasoning and machine learning},
  author={Barber, David},
  year={2012},
  publisher={Cambridge University Press}
}


@book{augustin2014introduction,
  title     = {Introduction to Imprecise Probabilities},
  publisher = {John Wiley},
  address = {Chichester},
  year      = {2014},
  editor    = {Thomas Augustin and Frank P. Coolen and Gert de Cooman and Matthias C. M. Troffaes}}


@inproceedings{street1993nuclear,
  title={Nuclear feature extraction for breast tumor diagnosis},
  author={Street, W Nick and Wolberg, William H and Mangasarian, Olvi L},
  booktitle={Biomedical image processing and biomedical visualization},
  volume={1905},
  pages={861--870},
  year={1993},
  organization={SPIE}
}

@article{gorman1988analysis,
  title={Analysis of hidden units in a layered network trained to classify sonar targets},
  author={Gorman, R Paul and Sejnowski, Terrence J},
  journal={Neural Networks},
  volume={1},
  number={1},
  pages={75--89},
  year={1988},
  publisher={Elsevier}
}

@article{sigillito1989classification,
  title={Classification of radar returns from the ionosphere using neural networks},
  author={Sigillito, Vincent G and Wing, Simon P and Hutton, Larrie V and Baker, Kile B},
  journal={Johns Hopkins Applied Physics Laboratory Technical Digest},
  volume={10},
  number={3},
  pages={262--266},
  year={1989}
}




@Manual{r-datasets,
    title = {datasets},
    note={package ‘datasets’ version 4.3.0},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2013},
    url = {http://www.R-project.org/},
  }

@book{schlimmer1987concept,
  title={Concept acquisition through representational adjustment},
  author={Schlimmer, Jeffrey Curtis},
  year={1987},
  publisher={University of California, Irvine}
}

@inproceedings{rodemann2022levelwise,
  title={Levelwise Data Disambiguation by Cautious Superset Classification},
  author={Rodemann, Julian and Kreiss, Dominik and H{\"u}llermeier, Eyke and Augustin, Thomas},
  booktitle={International Conference on Scalable Uncertainty Management},
  pages={263--276},
  year={2022},
  organization={Springer}
}


@phdthesis{waugh1995extending,
  title={Extending and benchmarking Cascade-Correlation: extensions to the Cascade-Correlation architecture and benchmarking of feed-forward supervised artificial neural networks},
  author={Waugh, Samuel George},
  year={1995},
  school={University of Tasmania}
}

@article{nelder1972generalized,
  title={Generalized linear models},
  author={Nelder, John Ashworth and Wedderburn, Robert WM},
  journal={Journal of the Royal Statistical Society: Series A (General)},
  volume={135},
  number={3},
  pages={370--384},
  year={1972},
  publisher={Wiley Online Library}
}

@incollection{hastie2017generalized,
  title={Generalized additive models},
  author={Hastie, Trevor},
    editor={Chambers, John M and Hastie, Trevor },
  booktitle={Statistical models in S},
  pages={249--307},
  year={2017},
  publisher={Routledge}
}
@article{fahrmeir2013regression,
  title={Regression: Models, Methods and Applications},
  author={Fahrmeir, Ludwig and Kneib, Thomas and Lang, Stefan and Marx, Brian},
  year={2013},
  publisher={Berlin, Heidelberg: Springer Berlin Heidelberg}
}



@book{gauss1877theoria,
  title={Theoria motus corporum coelestium in sectionibus conicis solem ambientium},
  author={Gau{\ss}, Carl Friedrich},
  volume={7},
  year={1877},
  publisher={FA Perthes}
}



@article{hurwicz1951generalized,
  title={The generalized {B}ayes minimax principle: a criterion for decision making under uncertainty},
  author={Hurwicz, Leonid},
  journal={Cowles Commission Discussion Paper Statistics},
  volume={335},
  pages={1950},
  year={1951}
}



@article{triguero2015self,
  title={Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study},
  author={Triguero, Isaac and Garc{\'\i}a, Salvador and Herrera, Francisco},
  journal={Knowledge and Information Systems},
  volume={42},
  number={2},
  pages={245--284},
  year={2015},
  publisher={Springer}
}

@inproceedings{Berthelot2019,
  author   = {Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin A},
  booktitle  = {Advances in Neural Information Processing Systems},
  title    = {Mixmatch: A holistic approach to semi-supervised learning},
  year     = {2019},
  volume   = {32},
  file     = {:Berthelot2019.pdf:PDF},
  keywords = {ssl, book chapter, book, ada},
}


@InProceedings{andriushchenko22a,
  title = 	 {Towards Understanding Sharpness-Aware Minimization},
  author =       {Andriushchenko, Maksym and Flammarion, Nicolas},
  booktitle = 	 {International Conference on Machine Learning},
  pages = 	 {639--668},
  year = 	 {2022},
  }


@book{zinn2021quantum,
  title={Quantum field theory and critical phenomena},
  author={Zinn-Justin, Jean},
  volume={171},
  year={2021},
  publisher={Oxford University Press}
}



@inproceedings{Sohn2020,
  title={Fixmatch: Simplifying semi-supervised learning with consistency and confidence},
  author={Sohn, Kihyuk and Berthelot, David and Carlini, Nicholas and Zhang, Zizhao and Zhang, Han and Raffel, Colin A and Cubuk, Ekin Dogus and Kurakin, Alexey and Li, Chun-Liang},
  booktitle={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}


@InProceedings{Zhang2021c,
  author    = {Zhang, Bowen and Wang, Yidong and Hou, Wenxin and WU, HAO and Wang, Jindong and Okumura, Manabu and Shinozaki, Takahiro},
  booktitle = {Advances in Neural Information Processing Systems},
  title     = {FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling},
  year      = {2021},
  pages     = {18408--18419},
  volume    = {34},
  file      = {:Zhang2021c.pdf:PDF},
  keywords  = {cc, constraintmatch, sscc, scc, zsolt, curriculum, learning, phd, related, work},
  url       = {https://proceedings.neurips.cc/paper/2021/file/995693c15f439e3d189b06e89d145dd5-Paper.pdf},
}


@book{bishop2006pattern,
  title={Pattern recognition and machine learning},
  author={Bishop, Christopher and Nasrabadi, Nasser},
  volume={4},
  number={4},
  year={2006},
  publisher={Springer}
}

@inproceedings{blum1998combining,
  title={Combining labeled and unlabeled data with co-training},
  author={Blum, Avrim and Mitchell, Tom},
  booktitle={Proceedings of the eleventh annual conference on Computational learning theory},
  pages={92--100},
  year={1998}
}

@misc{Dua:2019 ,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
note = {\url{http://archive.ics.uci.edu/ml}},
institution = "University of California, Irvine, School of Information and Computer Sciences" } 

@inproceedings{lee2013pseudo,
  title={Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks},
  author={Lee, Dong-Hyun},
  booktitle={Workshop on challenges in representation learning, International Conference on Machine Learning},
  volume={3},
  year={2013}
}

@inproceedings{li2020pseudo,
  title={Pseudo-label selection for deep semi-supervised learning},
  author={Li, Shuangshuang and Wei, Zhihui and Zhang, Jun and Xiao, Liang},
  booktitle={2020 IEEE International Conference on Progress in Informatics and Computing (PIC)},
  pages={1--5},
  year={2020},
  organization={IEEE}
}

@book{berger1985statistical,
  title={Statistical decision theory and {B}ayesian analysis.},
  author={Berger, James O.},
  journal={Springer Series in Statistics},
  year={1985},
  edition={2nd},
  publisher={Springer, Berlin.}
}


@inproceedings{patel2022seq,
  title={Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition},
  author={Patel, Gaurav and Allebach, Jan P and Qiu, Qiang},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={6180--6190},
  year={2023}
}


@book{koflerentscheidungen,
  title={Entscheidungen bei unvollst{\"a}ndiger Information},
  author={Kofler, Eduard and Menges, G{\"u}nter},
  year={1976},
  publisher={Springer}
}

@article{hastie1987generalized,
  title={Generalized additive models: some applications},
  author={Hastie, Trevor and Tibshirani, Robert},
  journal={Journal of the American Statistical Association},
  volume={82},
  number={398},
  pages={371--386},
  year={1987},
  publisher={Taylor \& Francis}
}



@article{evans2006checking,
  title={Checking for prior-data conflict},
  author={Evans, Michael and Moshonov, Hadas},
  journal={{B}ayesian analysis},
  volume={1},
  number={4},
  pages={893--914},
  year={2006},
  publisher={International Society for {B}ayesian Analysis}
}


@inproceedings{mcclosky2006effective,
  title={Effective self-training for parsing},
  author={McClosky, David and Charniak, Eugene and Johnson, Mark},
  booktitle={Proceedings of the Human Language Technology Conference of the NAACL, Main Conference},
  pages={152--159},
  year={2006}
}
@inproceedings{shi2018transductive,
  title={Transductive semi-supervised deep learning using min-max features},
  author={Shi, Weiwei and Gong, Yihong and Ding, Chris and Tao, Zhiheng MaXiaoyu and Zheng, Nanning},
  booktitle={European Conference on Computer Vision},
  pages={299--315},
  year={2018}
}
@article{ellsberg1961risk,
  title={Risk, ambiguity, and the Savage axioms},
  author={Ellsberg, Daniel},
  journal={The Quarterly Journal of Economics},
  pages={643--669},
  volume={75},
  number={4},
  year={1961},
  publisher={JSTOR}
}

@article{guo2010decision,
  title={Decision making with interval probabilities},
  author={Guo, Peijun and Tanaka, Hideo},
  journal={European Journal of Operational Research},
  volume={203},
  number={2},
  pages={444--454},
  year={2010},
  publisher={Elsevier}
}



@article{Plass:et:al:2019:IntStatRev,
  year = {2019},
  volume = {87},
  pages = {580--603},
  author = {J. Plass and M. Cattaneo and T. Augustin and G. Schollmeyer and C. Heumann},
  title = {Reliable Inference in Categorical Regression Analysis for Non-randomly Coarsened Observations},
  journal = {International Statistical Review}
}

@book{manski2003,
  title={Partial Identification of Probability Distributions},
  author={Manski, Charles},
  year={2003},
  publisher={Springer}
}

@book{Molchanov:Molinari:2018,
  title={Random Sets in Econometrics},
  author={Molchanov, I. and Molinari,F.},
  year={2018},
  publisher={Cambridge University Press}
}


@ARTICLE{2011:ponomareva,
  author = {Ponomareva, M. and Tamer, E.},
  title = {Misspecification in moment inequality models: back to moment equalities?},
  journal = {The Econometrics Journal},
  year = {2011},
  volume = {14},
  pages = {186--203}
}


@article{schollmeyer:15,
  title={Statistical modeling under partial identification: Distinguishing three types of identification regions in regression analysis with interval data},
  author={Schollmeyer, G. and Augustin, T.},
  journal={International Journal of Approximate Reasoning},
  volume={56},
  pages={224--248},
  year={2015},
}


@article{cortes1995support,
  title={Support-vector networks},
  author={Cortes, Corinna and Vapnik, Vladimir},
  journal={Machine Learning},
  volume={20},
  number={3},
  pages={273--297},
  year={1995},
  publisher={Springer}
}
@inproceedings{boser1992training,
  title={A training algorithm for optimal margin classifiers},
  author={Boser, Bernhard and Guyon, Isabelle and Vapnik, Vladimir},
  booktitle={Fifth Annual Workshop on Computational Learning Theory},
  pages={144--152},
  year={1992}
}
@article{hullermeier2014learning,
  title={Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization},
  author={H{\"u}llermeier, Eyke},
  journal={International Journal of Approximate Reasoning},
  volume={55},
  pages={1519--1534},
  year={2014},
  publisher={Elsevier}
} %number={1},

@inproceedings{hullermeier2019learning,
  title={Learning from imprecise data: adjustments of optimistic and pessimistic variants},
  author={H{\"u}llermeier, Eyke and Destercke, S{\'e}bastien and Couso, Ines},
  booktitle={International Conference on Scalable Uncertainty Management},
  pages={266--279},
  year={2019},
  publisher={Springer}
}

@article{karatzoglou2004kernlab,
  title={kernlab-an {S}4 package for kernel methods in {R}},
  author={Karatzoglou, Alexandros and Smola, Alex and Hornik, Kurt and Zeileis, Achim},
  journal={{Journal of Statistical Software}},
  volume={11},
  number={9},
  pages={1--20},
  year={2004},
  publisher={American Statistical Association}
}

@inproceedings{rodemann2022supersetlearning,
title={Levelwise Data Disambiguation by Cautious Superset Learning},
author={Rodemann, Julian and Kreiss, Dominik and Hüllermeier, Eyke and Augustin, Thomas},
booktitle={International Conference on Scalable Uncertainty Management},
year={2022},
publisher={Springer}
}

@book{chapelle2006semi,
  title={Semi-supervised learning.},
  editor={Chapelle, Olivier and Sch{\"o}lkopf, Bernhard and Zien, Alexander},
  year={2006},
  publisher={MIT Press}
}


@inproceedings{sokolovska2008asymptotics,
  title={The asymptotics of semi-supervised learning in discriminative probabilistic models},
  author={Sokolovska, Nataliya and Capp{\'e}, Olivier and Yvon, Fran{\c{c}}ois},
  booktitle={25th International Conference on Machine learning},
  pages={984--991},
  year={2008}
}

@article{kawakita2014safe,
  title={Safe semi-supervised learning based on weighted likelihood},
  author={Kawakita, Masanori and Takeuchi, Jun’ichi},
  journal={Neural Networks},
  volume={53},
  pages={146--164},
  year={2014},
  publisher={Elsevier}
}

@inproceedings{grandvalet2004semi,
  title={Semi-supervised learning by entropy minimization},
  author={Grandvalet, Yves and Bengio, Yoshua},
  booktitle={Advances in Neural Information Processing Systems},
  volume={17},
  year={2004}
}

@inproceedings{cohen2003semi,
  title={Semi-supervised learning for facial expression recognition},
  author={Cohen, Ira and Sebe, Nicu and Cozman, Fabio G and Huang, Thomas S},
  booktitle={5th ACM SIGMM International Workshop on Multimedia Information Retrieval},
  pages={17--22},
  year={2003}
}

@INPROCEEDINGS{cameloetal,  author={Camelo, Miguel and Shahid, Adnan and Fontaine, Jaron and de Figueiredo, Felipe Augusto Pereira and De Poorter, Eli and Moerman, Ingrid and Latre, Steven},  booktitle={2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)},   title={A semi-supervised learning approach towards automatic wireless technology recognition},   year={2019},  volume={},  number={},  pages={1-10},  doi={10.1109/DySPAN.2019.8935690}}



@article{llorente2020marginal,
  title={Marginal likelihood computation for model selection and hypothesis testing: an extensive review},
  author={Llorente, Fernando and Martino, Luca and Delgado, David and Lopez-Santiago, Javier},
  journal={SIAM Review},
  volume={65},
  number={1},
  pages={3--58},
  year={2023},
  publisher={SIAM}
}


@inproceedings{dinh2017sharp,
  title={Sharp minima can generalize for deep nets},
  author={Dinh, Laurent and Pascanu, Razvan and Bengio, Samy and Bengio, Yoshua},
  booktitle={International Conference on Machine Learning},
  pages={1019--1028},
  year={2017},
  organization={PMLR}
}

@inproceedings{li2018visualizing,
  title={Visualizing the loss landscape of neural nets},
  author={Li, Hao and Xu, Zheng and Taylor, Gavin and Studer, Christoph and Goldstein, Tom},
  booktitle={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}


@InProceedings{lotfi2022bayesian,
  title = 	 {{B}ayesian Model Selection, the Marginal Likelihood, and Generalization},
  author =       {Lotfi, Sanae and Izmailov, Pavel and Benton, Gregory and Goldblum, Micah and Wilson, Andrew Gordon},
  booktitle = 	 {International Conference on Machine Learning},
  pages = 	 {14223--14247},
  year = 	 {2022},
  pdf = 	 {https://proceedings.mlr.press/v162/lotfi22a/lotfi22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/lotfi22a.html},

}



@article{dorigatti2022positive,
  title={Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection},
  author={Dorigatti, Emilio and Goschenhofer, Jann and Schubert, Benjamin and Rezaei, Mina and Bischl, Bernd},
  journal={arXiv preprint arXiv:2201.13192},
  year={2022}
}

@article{zhang1995event,
  title={Event related potentials during object recognition tasks},
  author={Zhang, Xiao Lei and Begleiter, Henri and Porjesz, Bernice and Wang, Wenyu and Litke, Ann},
  journal={Brain Research Bulletin},
  volume={38},
  number={6},
  pages={531--538},
  year={1995},
  publisher={Elsevier}
}

@article{ezekiel1930methods,
  title={Methods of correlation analysis.},
  author={Ezekiel, Mordecai},
  year={1930},
  publisher={Wiley}
}

@inproceedings{in-all-likelihoods,
  title={In all Likelihoods: Robust Selection of Pseudo-Labeled Data},
  author={Rodemann, Julian and Jansen, Christoph and Schollmeyer,
Georg and Augustin, Thomas},
  booktitle={International Symposium on Imprecise Probabilities
Theories and Applications (ISIPTA)},
  organization={PMLR},
  year={2023},
  note={\textit{To appear}}
}


@inproceedings{rizve2020defense,
  title={In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning},
  author={Rizve, Mamshad Nayeem and Duarte, Kevin and Rawat, Yogesh S and Shah, Mubarak},
  booktitle={International Conference on Learning Representations, 2020},
  year={2020}
}

@inproceedings{aminian2022information,
  title={An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift},
  author={Aminian, Gholamali and Abroshan, Mahed and Khalili, Mohammad Mahdi and Toni, Laura and Rodrigues, Miguel},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={7433--7449},
  year={2022},
  organization={PMLR}
}
@article{vandewalle2013predictive,
  title={A predictive deviance criterion for selecting a generative model in semi-supervised classification},
  author={Vandewalle, Vincent and Biernacki, Christophe and Celeux, Gilles and Govaert, G{\'e}rard},
  journal={Computational Statistics \& Data Analysis},
  volume={64},
  pages={220--236},
  year={2013},
  publisher={Elsevier}
}


@inproceedings{amini2002semi,
  title={Semi-Supervised Logistic Regression},
  author={Amini, Massih-Reza and Gallinari, Patrick},
  booktitle={15th European Conference on Artificial Intelligence (ECAI 2002)},
  pages={390--394},
  year={2002}
}

@book{murphy2012machine,
  title={Machine learning: a probabilistic perspective},
  author={Murphy, Kevin P},
  year={2012},
  publisher={MIT press}
}



@inproceedings{adams2009archipelago,
  title={Archipelago: nonparametric {B}ayesian semi-supervised learning},
  author={Adams, Ryan Prescott and Ghahramani, Zoubin},
  booktitle={26th International Conference on Machine Learning},
  pages={1--8},
  year={2009}
}


@article{cai2011bassum,
  title={{BASSUM}: A {B}ayesian semi-supervised method for classification feature selection},
  author={Cai, Ruichu and Zhang, Zhenjie and Hao, Zhifeng},
  journal={Pattern Recognition},
  volume={44},
  number={4},
  pages={811--820},
  year={2011},
  publisher={Elsevier}
}

@article{tibshirani1996regression,
  title={Regression shrinkage and selection via the lasso},
  author={Tibshirani, Robert},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={58},
  number={1},
  pages={267--288},
  year={1996},
  publisher={Wiley Online Library}
}


@inproceedings{ng2018bayesian,
  title={{B}ayesian semi-supervised learning with graph {G}aussian processes},
  author={Ng, Yin Cheng and Colombo, Nicol{\`o} and Silva, Ricardo},
  booktitle={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}


@article{gordon2020combining,
  title={Combining deep generative and discriminative models for {B}ayesian semi-supervised learning},
  author={Gordon, Jonathan and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
  journal={Pattern Recognition},
  volume={100},
  pages={107156},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{lienen2021credal,
  title={Credal Self-Supervised Learning},
  author={Lienen, Julian and H{\"u}llermeier, Eyke},
  booktitle={Advances in Neural Information Processing Systems},
  volume={34},
  pages={14370--14382},
  year={2021}
}
@article{hullermeier2021aleatoric,
  title={Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods},
  author={H{\"u}llermeier, Eyke and Waegeman, Willem},
  journal={Machine Learning},
  volume={110},
  number={3},
  pages={457--506},
  year={2021},
  publisher={Springer}
}

@article{kostopoulos2018semi,
  title={Semi-supervised regression: A recent review},
  author={Kostopoulos, Georgios and Karlos, Stamatis and Kotsiantis, Sotiris and Ragos, Omiros},
  journal={Journal of Intelligent \& Fuzzy Systems},
  volume={35},
  number={2},
  pages={1483--1500},
  year={2018},
  publisher={IOS Press}
}



@article{faas2019more,
  title={The more things change, the more they stay the same? {T}he {G}erman federal election of 2017 and its consequences},
  author={Faas, Thorsten and Klingelh{\"o}fer, Tristan},
  journal={West European Politics},
  volume={42},
  number={4},
  pages={914--926},
  year={2019},
  publisher={Taylor \& Francis}
}


@article{kmeans-origin,
  title={Least squares quantization in {P}{C}{M}},
  author={Lloyd, Stuart},
  journal={IEEE Transactions on Information Theory},
  volume={28},
   pages={129--137},
  year={1982},
  publisher={IEEE}
}
%number={2},

@mastersthesis{rodemann2021robust,
  title={Robust Generalizations of Stochastic Derivative-Free Optimization},
  author={Rodemann, Julian},
  year={2021},
  school={LMU Munich}
}

@inproceedings{arazo2020pseudo,
  title={Pseudo-labeling and confirmation bias in deep semi-supervised learning},
  author={Arazo, Eric and Ortego, Diego and Albert, Paul and O’Connor, Noel E and McGuinness, Kevin},
  booktitle={2020 International Joint Conference on Neural Networks},
  pages={1--8},
  year={2020},
  organization={IEEE}
}

@inproceedings{cascante2021curriculum,
  title={Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning},
  author={Cascante-Bonilla, Paola and Tan, Fuwen and Qi, Yanjun and Ordonez, Vicente},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={8},
  pages={6912--6920},
  year={2021}
}


@incollection{insua2012robust,
  title={Robust {B}ayesian analysis},
  author={Ruggeri, Fabrizio and Insua, David R{\'\i}os and Mart{\'\i}n, Jacinto},
  journal={Handbook of Statistics},
  volume={25},
  pages={623--667},
  year={2005},
  publisher={Elsevier}
}

@book{rapoport1998decision,
  title={Decision theory and decision behaviour},
  author={Rapoport, Anatol},
  year={1998},
  publisher={Springer}
}



@inproceedings{hullermeier2015superset,
  title={Superset learning based on generalized loss minimization},
  author={H{\"u}llermeier, Eyke and Cheng, Weiwei},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={260--275},
  year={2015},
  organization={Springer}
}

@inproceedings{hinge98,
  title={Linear {H}inge loss and average margin},
  author={Gentile, Claudio and Warmuth, Manfred},
  booktitle={Advances in Neural Information Processing Systems},
  volume={11},
  year={1998},
  pages = {},
 publisher = {MIT Press}
}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Quellen DK
@InCollection{Kreiss2020,
  author        = {Kreiss, Dominik and Augustin, Thomas},
  title         = {Undecided Voters as Set-Valued Information, Towards Forecasts under Epistemic Imprecision},
  booktitle={International Conference on Scalable Uncertainty Management},
  publisher = {Springer},
  year          = {2020},
   pages={242--250}
}

@InCollection{Kreiss2020b,
  author        = {Kreiss, Dominik and Nalenz, Malte and Augustin, Thomas},
  title         = {Undecided Voters as Set-Valued Information, Machine Learning Approaches under Complex Uncertainty},
  booktitle = {ECML/PKDD 2020 Tutorial and Workshop on Uncertainty in Machine Learning},
  year          = {2020},
}

@InProceedings{Kreiss2021,
author = {Kreiss, Dominik and Schollmeyer, Georg and Augustin,
Thomas},
title= {Towards Improving Electoral Forecasting by Including Undecided
Voters and Interval-Valued Prior Knowledge},
booktitle =      {Proceedings of the Twelfth International Symposium
on
Imprecise Probabilities: Theories and Applications},
   year =      {2021},
   series =      {Proceedings of Machine Learning Research},
   address =      {Granada, Spain},
   month =      {06--09 Jul},
   publisher =    {PMLR},
}


@article{Kreiss2021b,
  title={Towards a Paradigmatic Shift in Pre-election Polling Adequately Including Still Undecided Voters--Some Ideas Based on Set-Valued Data for the 2021 {G}erman Federal Election},
  author={Kreiss, Dominik and Augustin, Thomas},
  journal={arXiv preprint arXiv:2109.12069},
  year={2021}
}

@Article{Couso2014a,
  author        = {Couso, In{\'e}s and Dubois, Didier},
  title         = {Statistical Reasoning with Set-Valued Information: Ontic vs. epistemic Views},
  journal       = {International Journal of Approximate Reasoning},
  year          = {2014},
  volume        = {55},
  pages         = {1502--1518},
  __markedentry = {[dominikkreiss:4]},
  keywords      = {rank5},
  publisher     = {Elsevier},
} %number        = {7},

@InCollection{Plass2015,
  author        = {Plass, Julia and Fink, Paul and Schöning, Norbert and Augustin, Thomas},
  title         = {Statistical Modelling in Surveys without Neglecting `{T}he Undecided`},
  year          = {2015},
  pages         = {257--266},
  booktitle = {{ISIPTA 15}},
  publisher = {{SIPTA}},
}

@Article{Dempster1967,
  author={Dempster, Arthur},
  title     = {Upper and Lower Probabilities Induced by a Multivalued Mapping},
  journal   = {The Annals of Mathematical Statistics},
  year      = {1967},
  volume    = {38},
  number    = {2},
  pages     = {325--339},
  fjournal  = {The Annals of Mathematical Statistics},
  publisher = {The Institute of Mathematical Statistics},
}

@article{heitjan1991ignorability,
  title={Ignorability and coarse data},
  author={Heitjan, Daniel and Rubin, Donald},
  journal={The Annals of Statistics},
  pages={2244--2253},
  year={1991},
  publisher={JSTOR}
}

@Article{Oscarsson2019a,
  author    = {Henrik Oscarsson and Maria Oskarson},
  title     = {Sequential Vote Choice: Applying a Consideration Set Model of Heterogeneous Decision Processes},
  journal   = {Electoral Studies},
  year      = {2019},
  volume    = {57},
  pages     = {275--283},
  month     = {feb},
  publisher = {Elsevier {BV}},
}



@article{shocker1991consideration,
  title={Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions},
  author={Shocker, Allan D and Ben-Akiva, Moshe and Boccara, Bruno and Nedungadi, Prakash},
  journal={Marketing Letters},
  volume={2},
  number={3},
  pages={181--197},
  year={1991},
  publisher={Springer}
}

@article{tversky1972,
  title={Elimination by aspects: A theory of choice.},
  author={Tversky, Amos},
  journal={Psychological Review},
  volume={79},
  number={4},
  pages={281},
  year={1972},
  publisher={American Psychological Association}
}


@inproceedings{liu2012conditional,
  title={A conditional multinomial mixture model for superset label learning},
  authors={Liu, Liping and Dietterich, Thomas},
  booktitle={Advances in Neural Information Processing Systems},
  volume={25},
  year={2012},
  pages = {}

}


@inproceedings{Tang_Zhang_2017,
title={Confidence-Rated Discriminative Partial Label Learning},
volume={31},
number={1}, 
booktitle={AAAI Conference on Artificial Intelligence},
author={Tang, Cai-Zhi and Zhang, Min-Ling},
year={2017}
}

@article{zhang2017disambiguation,
  title={Disambiguation-free partial label learning},
  author={Zhang, Min-Ling and Yu, Fei and Tang, Cai-Zhi},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={29},
  number={10},
  pages={2155--2167},
  year={2017},
  publisher={IEEE}
}

@inproceedings{nguyen2008classification,
  title={Classification with partial labels},
  author={Nguyen, Nam and Caruana, Rich},
  booktitle={Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={551--559},
  year={2008}
}

@article{couso2016machine,
  title={Machine learning models, Epistemic set-valued data and generalized loss functions: an encompassing approach},
  author={Couso, Ines and S{\'a}nchez, Luciano},
  journal={Information Sciences},
  volume={358},
  pages={129--150},
  year={2016},
  publisher={Elsevier}
}

@article{abdar2021review,
  title={A review of uncertainty quantification in deep learning: Techniques, applications and challenges},
  author={Abdar, Moloud and Pourpanah, Farhad and Hussain, Sadiq and Rezazadegan, Dana and Liu, Li and Ghavamzadeh, Mohammad and Fieguth, Paul and Cao, Xiaochun and Khosravi, Abbas and Acharya, U Rajendra and others},
  journal={Information Fusion},
  volume={76},
  pages={243--297},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{malinin2018predictive,
  title={Predictive uncertainty estimation via prior networks},
  author={Malinin, Andrey and Gales, Mark},
  booktitle={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}

@article{dempster1968generalization,
  title={A generalization of {B}ayesian inference},
  author={Dempster, Arthur P},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={30},
  number={2},
  pages={205--232},
  year={1968},
  publisher={Wiley Online Library}
}

@inproceedings{augustin2001decision,
  title={On Decision Making under Ambiguous Prior and Sampling Information.},
  author={Augustin, Thomas},
  booktitle={ISIPTA},
  volume={1},
  pages={9--16},
  year={2001}
}

@article{walter2009imprecision,
  title={Imprecision and prior-data conflict in generalized {B}ayesian inference},
  author={Walter, Gero and Augustin, Thomas},
  journal={Journal of Statistical Theory and Practice},
  volume={3},
  number={1},
  pages={255--271},
  year={2009},
  publisher={Taylor \& Francis}
}


@article{amini2022self,
  title={Self-Training: A Survey},
  author={Amini, Massih-Reza and Feofanov, Vasilii and Pauletto, Loic and Devijver, Emilie and Maximov, Yury},
  journal={arXiv preprint arXiv:2202.12040},
  year={2022}
}


@article{denoeux2011maximum,
  title={Maximum likelihood estimation from uncertain data in the belief function framework},
  author={Den{\oe}ux, Thierry},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={25},
  number={1},
  pages={119--130},
  year={2011},
  publisher={IEEE}
}

@article{denoeux2011maximum2,
  title={Maximum likelihood estimation from fuzzy data using the {EM} algorithm},
  author={Den{\oe}ux, Thierry},
  journal={Fuzzy Sets and Systems},
  volume={183},
   pages={72--91},
  year={2011},
  publisher={Elsevier}
} %number={1},

@inproceedings{liu2021cycle,
  title={Cycle self-training for domain adaptation},
  author={Liu, Hong and Wang, Jianmin and Long, Mingsheng},
  booktitle={Advances in Neural Information Processing Systems},
  volume={34},
  pages={22968--22981},
  year={2021}
}

@article{saporta2020esl,
  title={{ESL}: Entropy-guided self-supervised learning for domain adaptation in semantic segmentation},
  author={Saporta, Antoine and Vu, Tuan-Hung and Cord, Matthieu and P{\'e}rez, Patrick},
  journal={arXiv preprint arXiv:2006.08658},
  year={2020}
}

@article{chang2022pima,
  title={Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms},
  author={Chang, Victor and Bailey, Jozeene and Xu, Qianwen Ariel and Sun, Zhili},
  journal={Neural Computing and Applications},
  pages={1--17},
  year={2022},
  publisher={Springer}
}

@inproceedings{benzing2022gradient,
  title={Gradient descent on neurons and its link to approximate second-order optimization},
  author={Benzing, Frederik},
  booktitle={International Conference on Machine Learning},
  pages={1817--1853},
  year={2022},
  organization={PMLR}
}

@inproceedings{daxberger2021bayesian,
  title={{B}ayesian deep learning via subnetwork inference},
  author={Daxberger, Erik and Nalisnick, Eric and Allingham, James U and Antor{\'a}n, Javier and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
  booktitle={International Conference on Machine Learning},
  pages={2510--2521},
  year={2021},
  organization={PMLR}
}

@article{izmailov2021dangers,
  title={Dangers of {b}ayesian model averaging under covariate shift},
  author={Izmailov, Pavel and Nicholson, Patrick and Lotfi, Sanae and Wilson, Andrew G},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={3309--3322},
  year={2021}
}

@article{wenzel2020good,
  title={How good is the {B}ayes posterior in deep neural networks really?},
  author={Wenzel, Florian and Roth, Kevin and Veeling, Bastiaan S and {\'S}wi{\k{a}}tkowski, Jakub and Tran, Linh and Mandt, Stephan and Snoek, Jasper and Salimans, Tim and Jenatton, Rodolphe and Nowozin, Sebastian},
  journal={arXiv preprint arXiv:2002.02405},
  year={2020}
}

@article{demvsar2006statistical,
  title={Statistical comparisons of classifiers over multiple data sets},
  author={Dem{\v{s}}ar, Janez},
  journal={The Journal of Machine Learning Research},
  volume={7},
  pages={1--30},
  year={2006},
  publisher={JMLR. org}
}

@article{friedman1937use,
  title={The use of ranks to avoid the assumption of normality implicit in the analysis of variance},
  author={Friedman, Milton},
  journal={Journal of the American Statistical Association},
  volume={32},
  number={200},
  pages={675--701},
  year={1937},
  publisher={Taylor \& Francis}
}

@article{friedman1940comparison,
  title={A comparison of alternative tests of significance for the problem of m rankings},
  author={Friedman, Milton},
  journal={The Annals of Mathematical Statistics},
  volume={11},
  number={1},
  pages={86--92},
  year={1940},
  publisher={JSTOR}
}

@book{nemenyi1963distribution,
  title={Distribution-free multiple comparisons.},
  author={Nemenyi, Peter Bjorn},
  year={1963},
  publisher={Princeton University}
}

@book{wilcoxon1992individual,
  title={Individual comparisons by ranking methods},
  author={Wilcoxon, Frank},
  year={1992},
  publisher={Springer}
}

@unpublished{rodemann2022not,
  title={Not All Data Are Created Equal: Lessons From Sampling Theory For Adaptive Machine Learning},
  author={Rodemann, Julian and Fischer, Sebastian and Schneider, Lennart and Nalenz, Malte and Augustin, Thomas},
  year={2022},
  note={\textit{Poster presented at International Conference on Statistics and Data Science (ICSDS), Institute of Mathematical Statistics (IMS)}}
}

@inproceedings{giordano2019swiss,
  title={A {S}wiss {A}rmy infinitesimal jackknife},
  author={Giordano, Ryan and Stephenson, William and Liu, Runjing and Jordan, Michael and Broderick, Tamara},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={1139--1147},
  year={2019},
  organization={PMLR}
}

@inproceedings{jansen_uai2023,
  title={Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement},
  author={Jansen, Christoph and Schollmeyer, Georg and Blocher, Hannah and Rodemann, Julian and Augustin, Thomas},
  booktitle={Uncertainty in Artificial Intelligence (UAI)},
  year={2023},
  organization={PMLR},
  note={\textit{To appear}}
}

@article{jansen2022statistical,
  title={Statistical Comparisons of Classifiers by Generalized Stochastic Dominance},
  author={Jansen, Christoph and Nalenz, Malte and Schollmeyer, Georg and Augustin, Thomas},
  journal={arXiv preprint arXiv:2209.01857},
  year={2022}
}


@article{ionosphere,
  title={Classification of radar returns from the ionosphere using neural networks},
  author={Sigillito, Vincent G and Wing, Simon P and Hutton, Larrie V and Baker, Kile B},
  journal={Johns Hopkins APL Technical Digest},
  volume={10},
  number={3},
  pages={262--266},
  year={1989}
}


@article{cattaneo2013likelihood,
  title={Likelihood decision functions},
  author={Cattaneo, Marco EGV},
  journal={Electronic Journal of Statistics},
  volume={7},
  pages={2924--2946},
  year={2013}
}

@phdthesis{cattaneo2007statistical,
  title={Statistical decisions based directly on the likelihood function},
  author={Cattaneo, Marco EGV},
  year={2007},
  school={ETH Zurich}
}
