@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"
}

@inproceedings{linear_tree,
  title={Learning with continuous classes},
  author={Quinlan, John R and others},
  booktitle={5th Australian joint conference on artificial intelligence},
  volume={92},
  pages={343--348},
  year={1992},
  organization={World Scientific}
}

@article{lwlr,
  title={Locally weighted learning},
  author={Atkeson, Christopher G and Moore, Andrew W and Schaal, Stefan},
  journal={Lazy learning},
  pages={11--73},
  year={1997},
  publisher={Springer}
}

@article{XGBoost,
  author       = {Tianqi Chen and
                  Carlos Guestrin},
  title        = {XGBoost: {A} Scalable Tree Boosting System},
  journal      = {CoRR},
  volume       = {abs/1603.02754},
  year         = {2016},
  url          = {http://arxiv.org/abs/1603.02754},
  eprinttype    = {arXiv},
  eprint       = {1603.02754},
  timestamp    = {Sat, 17 Dec 2022 01:15:28 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/ChenG16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}


@article{lasso,
  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}
} 

@Article{cascading,
author={Gama, Jo{\~a}o
and Brazdil, Pavel},
title={Cascade Generalization},
journal={Machine Learning},
year={2000},
month={Dec},
day={01},
volume={41},
number={3},
pages={315-343},
abstract={Using multiple classifiers for increasing learning accuracy is an active research area. In this paper we present two related methods for merging classifiers. The first method, Cascade Generalization, couples classifiers loosely. It belongs to the family of stacking algorithms. The basic idea of Cascade Generalization is to use sequentially the set of classifiers, at each step performing an extension of the original data by the insertion of new attributes. The new attributes are derived from the probability class distribution given by a base classifier. This constructive step extends the representational language for the high level classifiers, relaxing their bias. The second method exploits tight coupling of classifiers, by applying Cascade Generalization locally. At each iteration of a divide and conquer algorithm, a reconstruction of the instance space occurs by the addition of new attributes. Each new attribute represents the probability that an example belongs to a class given by a base classifier. We have implemented three Local Generalization Algorithms. The first merges a linear discriminant with a decision tree, the second merges a naive Bayes with a decision tree, and the third merges a linear discriminant and a naive Bayes with a decision tree. All the algorithms show an increase of performance, when compared with the corresponding single models. Cascade also outperforms other methods for combining classifiers, like Stacked Generalization, and competes well against Boosting at statistically significant confidence levels.},
issn={1573-0565},
doi={10.1023/A:1007652114878},
url={https://doi.org/10.1023/A:1007652114878}
}



@article{stacking,
title = {Stacked generalization},
journal = {Neural Networks},
volume = {5},
number = {2},
pages = {241-259},
year = {1992},
issn = {0893-6080},
doi = {https://doi.org/10.1016/S0893-6080(05)80023-1},
url = {https://www.sciencedirect.com/science/article/pii/S0893608005800231},
author = {David H. Wolpert},
keywords = {Generalization and induction, Combining generalizers, Learning set preprocessing, cross-validation, Error estimation and correction},
abstract = {This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When used with multiple generalizers, stacked generalization can be seen as a more sophisticated version of cross-validation, exploiting a strategy more sophisticated than cross-validation's crude winner-takes-all for combining the individual generalizers. When used with a single generalizer, stacked generalization is a scheme for estimating (and then correcting for) the error of a generalizer which has been trained on a particular learning set and then asked a particular question. After introducing stacked generalization and justifying its use, this paper presents two numerical experiments. The first demonstrates how stacked generalization improves upon a set of separate generalizers for the NETtalk task of translating text to phonemes. The second demonstrates how stacked generalization improves the performance of a single surface-fitter. With the other experimental evidence in the literature, the usual arguments supporting cross-validation, and the abstract justifications presented in this paper, the conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate. This paper ends by discussing some of the variations of stacked generalization, and how it touches on other fields like chaos theory.}
}

@ARTICLE{ensemble2,
  author={Polikar, R.},
  journal={IEEE Circuits and Systems Magazine}, 
  title={Ensemble based systems in decision making}, 
  year={2006},
  volume={6},
  number={3},
  pages={21-45},
  doi={10.1109/MCAS.2006.1688199}}


@Article{samplecompress,
author={Floyd, Sally
and Warmuth, Manfred},
title={Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension},
journal={Machine Learning},
year={1995},
month={Dec},
day={01},
volume={21},
number={3},
pages={269-304},
abstract={Within the framework of pac-learning, we explore the learnability of concepts from samples using the paradigm of sample compression schemes. A sample compression scheme of size k for a concept class C{\$}{\$}{\backslash}subseteq {\$}{\$}2Xconsists of a compression function and a reconstruction function. The compression function receives a finite sample set consistent with some concept in C and chooses a subset of k examples as the compression set. The reconstruction function forms a hypothesis on X from a compression set of k examples. For any sample set of a concept in C the compression set produced by the compression function must lead to a hypothesis consistent with the whole original sample set when it is fed to the reconstruction function. We demonstrate that the existence of a sample compression scheme of fixed-size for a class C is sufficient to ensure that the class C is pac-learnable.},
issn={1573-0565},
doi={10.1023/A:1022660318680},
url={https://doi.org/10.1023/A:1022660318680}
}



@misc{deep,
  added-at = {2019-01-15T22:46:49.000+0100},
  author = {Nielsen, Michael A.},
  biburl = {https://www.bibsonomy.org/bibtex/274383acee84241145ff4ffede9658206/slicside},
  interhash = {04d527cadd39f888fc3babcad3343362},
  intrahash = {74383acee84241145ff4ffede9658206},
  keywords = {ba-2018-hahnrico},
  publisher = {Determination Press},
  timestamp = {2019-01-15T22:46:49.000+0100},
  title = {Neural Networks and Deep Learning},
  type = {misc},
  url = {http://neuralnetworksanddeeplearning.com/},
  year = 2018
}


@article{tsne,
  author  = {Laurens van der Maaten and Geoffrey Hinton},
  title   = {Visualizing Data using t-SNE},
  journal = {Journal of Machine Learning Research},
  year    = {2008},
  volume  = {9},
  number  = {86},
  pages   = {2579--2605},
  url     = {http://jmlr.org/papers/v9/vandermaaten08a.html}
}

@inproceedings{insta,
author = {Li, Ruey-Hsia and Belford, Geneva G.},
title = {Instability of Decision Tree Classification Algorithms},
year = {2002},
isbn = {158113567X},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/775047.775131},
doi = {10.1145/775047.775131},
abstract = {The instability problem of decision tree classification algorithms is that small changes in input training samples may cause dramatically large changes in output classification rules. Different rules generated from almost the same training samples are against human intuition and complicate the process of decision making. In this paper, we present fundamental theorems for the instability problem of decision tree classifiers. The first theorem gives the relationship between a data change and the resulting tree structure change (i.e. split change). The second theorem, Instability Theorem, provides the cause of the instability problem. Based on the two theorems, algorithmic improvements can be made to lessen the instability problem. Empirical results illustrate the theorem statements. The trees constructed by the proposed algorithm are more stable, noise-tolerant, informative, expressive, and concise. Our proposed sensitivity measure can be used as a metric to evaluate the stability of splitting predicates. The tree sensitivity is an indicator of the confidence level in rules and the effective lifetime of rules.},
booktitle = {Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {570–575},
numpages = {6},
location = {Edmonton, Alberta, Canada},
series = {KDD '02}
}

@article{omics2,
title = {Using machine learning approaches for multi-omics data analysis: A review},
journal = {Biotechnology Advances},
volume = {49},
pages = {107739},
year = {2021},
issn = {0734-9750},
doi = {https://doi.org/10.1016/j.biotechadv.2021.107739},
url = {https://www.sciencedirect.com/science/article/pii/S0734975021000458},
author = {Parminder S. Reel and Smarti Reel and Ewan Pearson and Emanuele Trucco and Emily Jefferson},
keywords = {Multi-omics, Machine Learning, Predictive Modelling, Supervised Learning, Unsupervised Learning, Systems Biology},
abstract = {With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.}
}

@inproceedings{Bach2004multiple,
	author = {Bach, Francis R. and Lanckriet, Gert R. G. and Jordan, Michael I.},
	title = {Multiple Kernel Learning, Conic Duality, and the SMO Algorithm},
	year = {2004},
	isbn = {1581138385},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/1015330.1015424},
	doi = {10.1145/1015330.1015424},
	abstract = {While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as a quadratically-constrained quadratic program (QCQP). Unfortunately, current convex optimization toolboxes can solve this problem only for a small number of kernels and a small number of data points; moreover, the sequential minimal optimization (SMO) techniques that are essential in large-scale implementations of the SVM cannot be applied because the cost function is non-differentiable. We propose a novel dual formulation of the QCQP as a second-order cone programming problem, and show how to exploit the technique of Moreau-Yosida regularization to yield a formulation to which SMO techniques can be applied. We present experimental results that show that our SMO-based algorithm is significantly more efficient than the general-purpose interior point methods available in current optimization toolboxes.},
	booktitle = {Proceedings of the Twenty-First International Conference on Machine Learning},
	pages = {6},
	location = {Banff, Alberta, Canada},
	series = {ICML '04}
}


@inproceedings{interpret,
	title={The coming of age of interpretable and explainable machine learning models},
	author={Lisboa, PJG and Saralajew, Sascha and Vellido, Alfredo and Villmann, Thomas},
	booktitle={ESANN 2021 proceedings European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
	year={2021},
	organization={ESANN Ciaco}
}

@inproceedings{lime,
	author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
	title = {"Why Should I Trust You?": Explaining the Predictions of Any Classifier},
	year = {2016},
	isbn = {9781450342322},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2939672.2939778},
	doi = {10.1145/2939672.2939778},
	abstract = {Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.},
	booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages = {1135–1144},
	numpages = {10},
	keywords = {interpretable machine learning, interpretability, explaining machine learning, black box classifier},
	location = {San Francisco, California, USA},
	series = {KDD '16}
}

@article{inter1,
	title={Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond},
	author={Li, Xuhong and Xiong, Haoyi and Li, Xingjian and Wu, Xuanyu and Zhang, Xiao and Liu, Ji and Bian, Jiang and Dou, Dejing},
	journal={Knowledge and Information Systems},
	pages={1--38},
	year={2022},
	publisher={Springer}
}

@article{inter2,
	title={Interpretable machine learning: Fundamental principles and 10 grand challenges},
	author={Rudin, Cynthia and Chen, Chaofan and Chen, Zhi and Huang, Haiyang and Semenova, Lesia and Zhong, Chudi},
	journal={Statistics Surveys},
	volume={16},
	pages={1--85},
	year={2022},
	publisher={Amer. Statist. Assoc., the Bernoulli Soc., the Inst. Math. Statist., and the~…}
}

@article{intercrit1,
	title={Towards robust interpretability with self-explaining neural networks},
	author={Alvarez Melis, David and Jaakkola, Tommi},
	journal={Advances in neural information processing systems},
	volume={31},
	year={2018}
}

@article{intercrit2,
	title={Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI},
	author={Arrieta, Alejandro Barredo and D{\'\i}az-Rodr{\'\i}guez, Natalia and Del Ser, Javier and Bennetot, Adrien and Tabik, Siham and Barbado, Alberto and Garc{\'\i}a, Salvador and Gil-L{\'o}pez, Sergio and Molina, Daniel and Benjamins, Richard and others},
	journal={Information fusion},
	volume={58},
	pages={82--115},
	year={2020},
	publisher={Elsevier}
}

@article{inter3,
	title={Definitions, methods, and applications in interpretable machine learning},
	author={Murdoch, W James and Singh, Chandan and Kumbier, Karl and Abbasi-Asl, Reza and Yu, Bin},
	journal={Proceedings of the National Academy of Sciences},
	volume={116},
	number={44},
	pages={22071--22080},
	year={2019},
	publisher={National Acad Sciences}
}

@inproceedings{inter4,
	title={Interpretable machine learning--a brief history, state-of-the-art and challenges},
	author={Molnar, Christoph and Casalicchio, Giuseppe and Bischl, Bernd},
	booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	pages={417--431},
	year={2020},
	organization={Springer}
}




@inproceedings{deeplift,
	title={Learning important features through propagating activation differences},
	author={Shrikumar, Avanti and Greenside, Peyton and Kundaje, Anshul},
	booktitle={International conference on machine learning},
	pages={3145--3153},
	year={2017},
	organization={PMLR}
}

@article{metriclearning,
	title={A principled approach to network-based classification and data representation},
	author={Ruiz, H{\'e}ctor and Etchells, Terence A and Jarman, Ian H and Mart{\'\i}n, Jos{\'e} D and Lisboa, Paulo JG},
	journal={Neurocomputing},
	volume={112},
	pages={79--91},
	year={2013},
	publisher={Elsevier}
}

@article{attention,
	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, {\L}ukasz and Polosukhin, Illia},
	journal={Advances in neural information processing systems},
	volume={30},
	year={2017}
}

@article{shapley,
	title={The shapley value},
	author={Winter, Eyal},
	journal={Handbook of game theory with economic applications},
	volume={3},
	pages={2025--2054},
	year={2002},
	publisher={Elsevier}
}


@Article{chapple,
	author={Chapple, Charles E.
	and Robisson, Benoit
	and Spinelli, Lionel
	and Guien, C{\'e}line
	and Becker, Emmanuelle
	and Brun, Christine},
	title={Extreme multifunctional proteins identified from a human protein interaction network},
	journal={Nature Communications},
	year={2015},
	month={Jun},
	day={09},
	volume={6},
	number={1},
	pages={7412},
	abstract={Moonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of `extreme multifunctional' proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3{\%} of the human interactome). We show that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins.},
	issn={2041-1723},
	doi={10.1038/ncomms8412},
	url={https://doi.org/10.1038/ncomms8412}
}


@article{imba,
	author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
	title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
	journal = {Journal of Machine Learning Research},
	year    = {2017},
	volume  = {18},
	number  = {17},
	pages   = {1-5},
	url     = {http://jmlr.org/papers/v18/16-365.html}
}

@Article{moondb,
	Author="Ribeiro, D. M.  and Briere, G.  and Bely, B.  and Spinelli, L.  and Brun, C. ",
	Title="{{M}oon{D}{B} 2.0: an updated database of extreme multifunctional and moonlighting proteins}",
	Journal="Nucleic Acids Res",
	Year="2019",
	Volume="47",
	Number="D1",
	Pages="D398-D402",
	Month="01"
}

@InProceedings{shared_boost,
	author="Peng, Jing
	and Barbu, Costin
	and Seetharaman, Guna
	and Fan, Wei
	and Wu, Xian
	and Palaniappan, Kannappan",
	editor="Gunopulos, Dimitrios
	and Hofmann, Thomas
	and Malerba, Donato
	and Vazirgiannis, Michalis",
	title="ShareBoost: Boosting for Multi-view Learning with Performance Guarantees",
	booktitle="Machine Learning and Knowledge Discovery in Databases",
	year="2011",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="597--612",
	abstract="Algorithms combining multi-view information are known to exponentially quicken classification, and have been applied to many fields. However, they lack the ability to mine most discriminant information sources (or data types) for making predictions. In this paper, we propose an algorithm based on boosting to address these problems. The proposed algorithm builds base classifiers independently from each data type (view) that provides a partial view about an object of interest. Different from AdaBoost, where each view has its own re-sampling weight, our algorithm uses a single re-sampling distribution for all views at each boosting round. This distribution is determined by the view whose training error is minimal. This shared sampling mechanism restricts noise to individual views, thereby reducing sensitivity to noise. Furthermore, in order to establish performance guarantees, we introduce a randomized version of the algorithm, where a winning view is chosen probabilistically. As a result, it can be cast within a multi-armed bandit framework, which allows us to show that with high probability the algorithm seeks out most discriminant views of data for making predictions. We provide experimental results that show its performance against noise and competing techniques.",
	isbn="978-3-642-23783-6"
}


@InProceedings{summit,
	title = 	 {Integrating and reporting full multi-view supervised learning experiments using SuMMIT},
	author =       {Bauvin, Baptiste and Corbeil, Jacques and Benielli, Dominique and Ko\c{c}o, Sokol and Capponi, Cecile},
	booktitle = 	 {Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications},
	pages = 	 {139--150},
	year = 	 {2022},
	editor = 	 {Moniz, Nuno and Branco, Paula and Torgo, Luís and Japkowicz, Nathalie and Wozniak, Michal and Wang, Shuo},
	volume = 	 {183},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {23 Sep},
	publisher =    {PMLR},
	pdf = 	 {https://proceedings.mlr.press/v183/bauvin22a/bauvin22a.pdf},
	url = 	 {https://proceedings.mlr.press/v183/bauvin22a.html}
}


@article{JMLR,
	author  = {Ronan Perry and Gavin Mischler and Richard Guo and Theodore Lee and Alexander Chang and Arman Koul and Cameron Franz and Hugo Richard and Iain Carmichael and Pierre Ablin and Alexandre Gramfort and Joshua T. Vogelstein},
	title   = {mvlearn: Multiview Machine Learning in Python},
	journal = {Journal of Machine Learning Research},
	year    = {2021},
	volume  = {22},
	number  = {109},
	pages   = {1-7},
	url     = {http://jmlr.org/papers/v22/20-1370.html}
}

@article{gb,
	ISSN = {00905364},
	URL = {http://www.jstor.org/stable/2699986},
	abstract = {Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent "boosting" paradigm is developed for additive expansions based on any fitting criterion. Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such "TreeBoost" models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.},
	author = {Jerome H. Friedman},
	journal = {The Annals of Statistics},
	number = {5},
	pages = {1189--1232},
	publisher = {Institute of Mathematical Statistics},
	title = {Greedy Function Approximation: A Gradient Boosting Machine},
	volume = {29},
	year = {2001}
}




}
@inproceedings{langford2003pac,
	title={PAC-Bayes \& margins},
	author={Langford, John and Shawe-Taylor, John},
	booktitle={Advances in neural information processing systems},
	pages={439--446},
	year={2003}
}

@inproceedings{germain2009pac,
	title={PAC-Bayesian learning of linear classifiers},
	author={Germain, Pascal and Lacasse, Alexandre and Laviolette, Fran{\c{c}}ois and Marchand, Mario},
	booktitle={Proceedings of the 26th ICML},
	pages={353--360},
	year={2009},
	organization={ACM}
}

@inproceedings{dziugaite2018data,
	title={Data-dependent PAC-Bayes priors via differential privacy},
	author={Dziugaite, Gintare Karolina and Roy, Daniel M},
	booktitle={Advances in Neural Information Processing Systems},
	pages={8440--8450},
	year={2018}
}

@inproceedings{seldin2012pac,
	title={PAC-Bayes-Bernstein inequality for martingales and its application to multiarmed bandits},
	author={Seldin, Yevgeny and Cesa-Bianchi, Nicol{\`o} and Auer, Peter and Laviolette, Fran{\c{c}}ois and Shawe-Taylor, John},
	booktitle={Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2},
	pages={98--111},
	year={2012}
}

@article{catoni2007pac,
	title={PAC-Bayesian supervised classification: the thermodynamics of statistical learning},
	author={Catoni, Olivier},
	journal={arXiv preprint arXiv:0712.0248},
	year={2007}
}

@article{seeger2002pac,
	title={Pac-bayesian generalisation error bounds for gaussian process classification},
	author={Seeger, Matthias},
	journal={J. Mach. Learn. Res.},
	volume={3},
	number={Oct},
	pages={233--269},
	year={2002}
}

@Article{McAllester2003,
	author="McAllester, David A.",
	title="PAC-Bayesian Stochastic Model Selection",
	journal="Machine Learning",
	year="2003",
	month="Apr",
	day="01",
	volume="51",
	number="1",
	pages="5--21"
}

@article{mincq,
	author = {Germain, Pascal and Lacasse, Alexandre and Laviolette, Fran\c{c}ois and Marchand, Mario and Roy, Jean-Francis},
	title = {Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm},
	journal = {J. Mach. Learn. Res.},
	issue_date = {January 2015},
	volume = {16},
	number = {1},
	month = jan,
	year = {2015},
	issn = {1532-4435},
	pages = {787--860},
	numpages = {74},
	acmid = {2831140},
	publisher = {JMLR.org},
	keywords = {PAC-Bayesian theory, ensemble methods, learning theory, majority vote, sample compression},
} 
url = {http://dl.acm.org/citation.cfm?id=2789272.2831140},

@article{valiant84,
	author    = {Leslie G. Valiant},
	title     = {A Theory of the Learnable},
	journal   = {Commun. {ACM}},
	volume    = {27},
	number    = {11},
	pages     = {1134--1142},
	year      = {1984}
}

@InProceedings{cqboost,
	title = 	 {A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees},
	author = 	 {Jean-Francis Roy and Mario Marchand and François Laviolette},
	booktitle = 	 {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
	pages = 	 {1241--1249},
	year = 	 {2016},
	editor = 	 {Arthur Gretton and Christian C. Robert},
	volume = 	 {51},
	series = 	 {Proceedings of Machine Learning Research},
	address = 	 {Cadiz, Spain},
	month = 	 {09--11 May},
	publisher = 	 {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v51/roy16.pdf},
	url = 	 {http://proceedings.mlr.press/v51/roy16.html},
	abstract = 	 {The C-bound, introduced in Lacasse et al (2006), gives a tight upper bound on the risk of the majority vote classifier. Laviolette et al. (2011) designed a learning algorithm named MinCq that outputs a dense distribution on a finite set of base classifiers by minimizing the C-bound, together with a PAC-Bayesian generalization guarantee. In this work, we design a column generation algorithm that we call CqBoost, that optimizes the C-bound and outputs a sparse distribution on a possibly infinite set of voters. We also propose a PAC-Bayesian bound for CqBoost that holds for finite and two cases of continuous sets of base classifiers. Finally, compare the accuracy and the sparsity of CqBoost with MinCq and other state-of-the-art boosting algorithms.}
}



@book{schapireboosting,
	author = {Schapire, Robert E. and Freund, Yoav},
	title = {Boosting: Foundations and Algorithms},
	year = {2012},
	isbn = {0262017180},
	publisher = {The MIT Press}
}

@article{mnist,
	added-at = {2010-06-28T21:16:30.000+0200},
	author = {LeCun, Yann and Cortes, Corinna},
	biburl = {https://www.bibsonomy.org/bibtex/2935bad99fa1f65e03c25b315aa3c1032/mhwombat},
	groups = {public},
	howpublished = {http://yann.lecun.com/exdb/mnist/},
	interhash = {21b9d0558bd66279df9452562df6e6f3},
	intrahash = {935bad99fa1f65e03c25b315aa3c1032},
	keywords = {MSc _checked character_recognition mnist network neural},
	lastchecked = {2016-01-14 14:24:11},
	timestamp = {2016-07-12T19:25:30.000+0200},
	title = {{MNIST} handwritten digit database},
	username = {mhwombat},
	year = 2010
}
url = {http://yann.lecun.com/exdb/mnist/},

@article{SCM,
	author = {Marchand, Mario and Taylor, John Shawe},
	title = {The Set Covering Machine},
	journal = {J. Mach. Learn. Res.},
	issue_date = {3/1/2003},
	volume = {3},
	month = mar,
	year = {2003},
	issn = {1532-4435},
	pages = {723--746},
	numpages = {24},
	acmid = {944952},
	publisher = {JMLR.org},
} 
url = {http://dl.acm.org/citation.cfm?id=944919.944952},

@article{lasso1,
	author = {Tibshirani, Rob and Hastie, Trevor and Friedman, Jerome},
	year = {2010},
	month = {02},
	pages = {},
	title = {Regularized Paths for Generalized Linear Models Via Coordinate Descent},
	volume = {33},
	journal = {Journal of Statistical Software},
}
doi = {10.1163/ej.9789004178922.i-328.7}

@article{lasso2,
	author = {Koh, Kwangmoo and Kim, Seung-Jean and Boyd, Stephen},
	title = {An Interior-Point Method for Large-Scale L1-Regularized Logistic Regression},
	journal = {J. Mach. Learn. Res.},
	issue_date = {12/1/2007},
	volume = {8},
	month = {dec},
	year = {2007},
	issn = {1532-4435},
	pages = {1519--1555},
	numpages = {37},
	acmid = {1314550},
	publisher = {JMLR.org},
} 
url = {http://dl.acm.org/citation.cfm?id=1314498.1314550},

@Article{RF,
	author="Breiman, Leo",
	title="Random Forests",
	journal="Machine Learning",
	year="2001",
	month="Oct",
	day="01",
	volume="45",
	number="1",
	pages="5--32",
	abstract="Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund {\&} R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148--156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.",
	issn="1573-0565",
	doi="10.1023/A:1010933404324",
	url="https://doi.org/10.1023/A:1010933404324"
}



@article{mkl1,
	author    = {S{\"{o}}ren Sonnenburg and
	Gunnar R{\"{a}}tsch and
	Christin Sch{\"{a}}fer and
	Bernhard Sch{\"{o}}lkopf},
	title     = {Large Scale Multiple Kernel Learning},
	journal   = {J. Mach. Learn. Res.},
	volume    = {7},
	pages     = {1531--1565},
	year      = {2006},
	timestamp = {Wed, 03 Apr 2019 18:34:01 +0200},
	biburl    = {https://dblp.org/rec/bib/journals/jmlr/SonnenburgRSS06},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}
url       = {http://jmlr.org/papers/v7/sonnenburg06a.html},

@article{mkl2,
	author = {Lanckriet, Gert R. G. and Cristianini, Nello and Bartlett, Peter and Ghaoui, Laurent El and Jordan, Michael I.},
	title = {Learning the Kernel Matrix with Semidefinite Programming},
	journal = {J. Mach. Learn. Res.},
	issue_date = {12/1/2004},
	volume = {5},
	month = dec,
	year = {2004},
	issn = {1532-4435},
	pages = {27--72},
	numpages = {46},
	acmid = {1005334},
	publisher = {JMLR.org},
} 
url = {http://dl.acm.org/citation.cfm?id=1005332.1005334},

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

@article{Parrado,
	author = {Parrado-Hern\'{a}ndez, Emilio and Ambroladze, Amiran and Shawe-Taylor, John and Sun, Shiliang},
	title = {PAC-bayes Bounds with Data Dependent Priors},
	journal = {J. Mach. Learn. Res.},
	issue_date = {January 2012},
	volume = {13},
	month = {12},
	year = {2012},
	issn = {1532-4435},
	pages = {3507--3531},
	numpages = {25},
	acmid = {2503353},
	publisher = {JMLR.org},
	keywords = {PAC-Bayes bound, classification, generalization capability prediction, support vector machine},
} 
url = {http://dl.acm.org/citation.cfm?id=2503308.2503353},

@inproceedings{deep_boost,
	title	= {Deep boosting},
	author	= {Corinna Cortes and Mehryar Mohri and Umar Syed},
	year	= {2014},
	booktitle	= {Proceedings of the Thirty-First International Conference on Machine Learning (ICML 2014)}
}

@article{adasamme,
	author = {Zhu, Ji and Rosset, Saharon and Zou, Hui and Hastie, Trevor},
	year = {2006},
	month = {02},
	pages = {},
	title = {Multi-class AdaBoost},
	volume = {2},
	journal = {Statistics and its interface},
	doi = {10.4310/SII.2009.v2.n3.a8}
}

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

@inproceedings{cnn,
	title={Handwritten digit recognition with a back-propagation network},
	author={LeCun, Yann and Boser, Bernhard E and Denker, John S and Henderson, Donnie and Howard, Richard E and Hubbard, Wayne E and Jackel, Lawrence D},
	booktitle={Advances in neural information processing systems},
	pages={396--404},
	year={1990}
}



@article{randomsearch,
	added-at = {2019-07-10T00:00:00.000+0200},
	author = {Bergstra, James and Bengio, Yoshua},
	biburl = {https://www.bibsonomy.org/bibtex/2c2b93293f3765f0c3e67be30a235f100/dblp},
	ee = {http://dl.acm.org/citation.cfm?id=2188395},
	interhash = {43621a41bae9d8c8c45c9a0e4b12f1a0},
	intrahash = {c2b93293f3765f0c3e67be30a235f100},
	journal = {J. Mach. Learn. Res.},
	keywords = {dblp},
	pages = {281-305},
	timestamp = {2019-07-11T11:39:48.000+0200},
	title = {Random Search for Hyper-Parameter Optimization.},
	url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlr13.html#BergstraB12},
	volume = 13,
	year = 2012
}

@ARTICLE{awa,	
	author={Xian, Yongqin and Lampert, Christoph H. and Schiele, Bernt and Akata, Zeynep},	
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 	
	title={Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly}, 	
	year={2019},	
	volume={41},	
	number={9},	
	pages={2251-2265},	
	doi={10.1109/TPAMI.2018.2857768}}

@book{perceptron,
	title={Neural networks: a comprehensive foundation},
	author={Haykin, Simon},
	year={1994},
	publisher={Prentice Hall PTR}
} 

@Article{path1,
	Author="Stark, C.  and Breitkreutz, B. J.  and Reguly, T.  and Boucher, L.  and Breitkreutz, A.  and Tyers, M. ",
	Title="{{B}io{G}{R}{I}{D}: a general repository for interaction datasets}",
	Journal="Nucleic Acids Res",
	Year="2006",
	Volume="34",
	Number="Database issue",
	Pages="D535--539",
	Month="Jan"
}

@inproceedings{groupscm,
	author = {Osseni, Mazid and Tossou, Prudencio and Corbeil, Jacques and Laviolette, Francois},
	year = {2021},
	month = {01},
	pages = {72-82},
	title = {Applying PySCMGroup to Breast Cancer Biomarkers Discovery},
	doi = {10.5220/0010375500720082}
}

@Article{path2,
	Author="Liberzon, A.  and Subramanian, A.  and Pinchback, R.  and Thorvaldsdóttir, H.  and Tamayo, P.  and Mesirov, J. P. ",
	Title="{{M}olecular signatures database ({M}{S}ig{D}{B}) 3.0}",
	Journal="Bioinformatics",
	Year="2011",
	Volume="27",
	Number="12",
	Pages="1739--1740",
	Month="Jun"
}


@Article{tnbc,
	author={Kothari, Charu
	and Osseni, Mazid Abiodoun
	and Agbo, Lynda
	and Ouellette, Genevi{\`e}ve
	and D{\'e}raspe, Maxime
	and Laviolette, Fran{\c{c}}ois
	and Corbeil, Jacques
	and Lambert, Jean-Philippe
	and Diorio, Caroline
	and Durocher, Francine},
	title={Machine learning analysis identifies genes differentiating triple negative breast cancers},
	journal={Scientific Reports},
	year={2020},
	month={Jun},
	day={26},
	volume={10},
	number={1},
	pages={10464},
	abstract={Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and have identified two potential genes, TBC1D9 (TBC1 domain family member 9) and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein), that could successfully differentiate TNBC from non-TNBC, irrespective of their heterogeneity. TBC1D9 is under-expressed in TNBC as compared to non-TNBC patients, while MFGE8 is over-expressed. Overexpression of TBC1D9 has a better prognosis whereas overexpression of MFGE8 correlates with a poor prognosis. Protein--protein interaction analysis by affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID) experiments identified a role for TBC1D9 in maintaining cellular integrity, whereas MFGE8 would be involved in various tumor survival processes. These promising genes could serve as biomarkers for TNBC and deserve further investigation as they have the potential to be developed as therapeutic targets for TNBC.},
	issn={2045-2322},
	doi={10.1038/s41598-020-67525-1},
	url={https://doi.org/10.1038/s41598-020-67525-1}
}


@article{recover,
	author = {Racine, Étienne and Boivin, Guy and Longtin, Yves and McCormack, Deirdre and Decaluwe, Hélène and Savard, Patrice and Cheng, Matthew P. and Hamelin, Marie-Ève and Carbonneau, Julie and Tadount, Fazia and Adams, Kelsey and Bourdin, Benoîte and Nantel, Sabryna and Gilca, Vladimir and Corbeil, Jacques and De Serres, Gaston and Quach-Thanh, Caroline},
	title = {The REinfection in COVID-19 Estimation of Risk (RECOVER) study: Reinfection and serology dynamics in a cohort of Canadian healthcare workers},
	journal = {Influenza and Other Respiratory Viruses},
	volume = {n/a},
	number = {n/a},
	pages = {},
	keywords = {COVID-19, prospective studies, reinfection, SARS-CoV-2, serology},
	doi = {https://doi.org/10.1111/irv.12997},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/irv.12997},
	eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/irv.12997},
	abstract = {Abstract Background Understanding the immune response to natural infection by SARS-CoV-2 is key to pandemic management, especially in the current context of emerging variants. Uncertainty remains regarding the efficacy and duration of natural immunity against reinfection. Methods We conducted an observational prospective cohort study in Canadian healthcare workers (HCWs) with a history of PCR-confirmed SARS-CoV-2 infection to (i) measure the average incidence rate of reinfection and (ii) describe the serological immune response to the primary infection. Results Our cohort comprised 569 HCWs; median duration of individual follow-up was 371 days. We detected six cases of reinfection in absence of vaccination between August 21, 2020, and March 1, 2022, for a reinfection incidence rate of 4.0 per 100 person-years. Median duration of seropositivity was 415 days in symptomatics at primary infection compared with 213 days in asymptomatics (p < 0.0001). Other characteristics associated with prolonged seropositivity for IgG against the spike protein included age over 55 years, obesity, and non-Caucasian ethnicity. Conclusions Among unvaccinated healthcare workers, reinfection with SARS-CoV-2 following a primary infection remained rare.}
}


@article{gridsearch,
	title={On the relationship between classical grid search and probabilistic roadmaps},
	author={LaValle, Steven M and Branicky, Michael S and Lindemann, Stephen R},
	journal={The International Journal of Robotics Research},
	volume={23},
	number={7-8},
	pages={673--692},
	year={2004},
	publisher={SAGE Publications}
}

@incollection{mucombo,
	TITLE = {{Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods}},
	AUTHOR = {Capponi, C{\'e}cile and Ko{\c c}o, Sokol},
	URL = {https://hal.archives-ouvertes.fr/hal-02068594},
	BOOKTITLE = {{Linking and Mining Heterogeneous and Multi-view Data}},
	EDITOR = {Springer},
	YEAR = {2019},
	MONTH = Jan,
	KEYWORDS = {Boosting ; Multi-view Learning ; Multi-class Learning ; Imbalanced Learning ; ensemble methods ; imbalanced classes ; confusion matrix norm ; boosting ; multi-view learning},
	PDF = {https://hal.archives-ouvertes.fr/hal-02068594/file/main_mucombo.pdf},
	HAL_ID = {hal-02068594},
	HAL_VERSION = {v1},
}

@inbook{kernel, 
	place={Cambridge}, 
	title={Basic concepts}, 
	booktitle={Kernel Methods for Pattern Analysis}, 
	publisher={Cambridge University Press}, 
	author={Shawe-Taylor, John and Cristianini, Nello}, 
	year={2004}}

@InProceedings{riikka,
	title = 	 {Multi-view Metric Learning in Vector-valued Kernel Spaces},
	author = 	 {Riikka Huusari and Hachem Kadri and Cécile Capponi},
	booktitle = 	 {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
	pages = 	 {415--424},
	year = 	 {2018},
	editor = 	 {Amos Storkey and Fernando Perez-Cruz},
	volume = 	 {84},
	series = 	 {Proceedings of Machine Learning Research},
	address = 	 {Playa Blanca, Lanzarote, Canary Islands},
	month = 	 {09--11 Apr},
	publisher = 	 {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v84/huusari18a/huusari18a.pdf},
	url = 	 {http://proceedings.mlr.press/v84/huusari18a.html}
}

@article{vecval,
	author = {Micchelli, Charles A. and Pontil, Massimiliano},
	title = "{On Learning Vector-Valued Functions}",
	journal = {Neural Computation},
	volume = {17},
	number = {1},
	pages = {177-204},
	year = {2005},
	month = {01},
	abstract = "{In this letter, we provide a study of learning in a Hilbert space of vector-valued functions. We motivate the need for extending learning theory of scalar-valued functions by practical considerations and establish some basic results for learning vector-valued functions that should prove useful in applications. Specifically, we allow an output space Y to be a Hilbert space, and we consider a reproducing kernel Hilbert space of functions whose values lie in Y. In this setting, we derive the form of the minimal norm interpolant to a finite set of data and apply it to study some regularization functionals that are important in learning theory. We consider specific examples of such functionals corresponding to multiple-output regularization networks and support vector machines, for both regression and classification. Finally, we provide classes of operator-valued kernels of the dot product and translation-invariant type.}",
	issn = {0899-7667},
	doi = {10.1162/0899766052530802},
	url = {https://doi.org/10.1162/0899766052530802},
	eprint = {https://direct.mit.edu/neco/article-pdf/17/1/177/816069/0899766052530802.pdf},
}

@inproceedings{search2,
	author = {Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger},
	pages = {},
	publisher = {Curran Associates, Inc.},
	title = {Practical Bayesian Optimization of Machine Learning Algorithms},
	url = {https://proceedings.neurips.cc/paper/2012/file/05311655a15b75fab86956663e1819cd-Paper.pdf},
	volume = {25},
	year = {2012}
}

@book{crossval,
	title={The elements of statistical learning: data mining, inference, and prediction},
	author={Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome H and Friedman, Jerome H},
	volume={2},
	chapter={7},
	year={2009},
	publisher={Springer}
}

@article{tensorfact,  
	author = {Tamara G. Kolda and Brett W. Bader}, 
	title = {Tensor Decompositions and Applications}, 
	journal = {SIAM Review}, 
	volume = {51}, 
	number = {3}, 
	pages = {455--500}, 
	month = {September}, 
	year = {2009},
	doi = {10.1137/07070111X},
}


@InProceedings{cotraining2,
	author="Ling, Charles X.
	and Du, Jun
	and Zhou, Zhi-Hua",
	editor="Theeramunkong, Thanaruk
	and Kijsirikul, Boonserm
	and Cercone, Nick
	and Ho, Tu-Bao",
	title="When does Co-training Work in Real Data?",
	booktitle="Advances in Knowledge Discovery and Data Mining",
	year="2009",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="596--603",
	abstract="Co-training, a paradigm of semi-supervised learning, may alleviate effectively the data scarcity problem (i.e., the lack of labeled examples) in supervised learning. The standard two-view co-training requires the dataset be described by two views of attributes, and previous theoretical studies proved that if the two views satisfy the sufficiency and independence assumptions, co-training is guaranteed to work well. However, little work has been done on how these assumptions can be empirically verified given datasets. In this paper, we first propose novel approaches to verify empirically the two assumptions of co-training based on datasets. We then propose simple heuristic to split a single view of attributes into two views, and discover regularity on the sufficiency and independence thresholds for the standard two-view co-training to work well. Our empirical results not only coincide well with the previous theoretical findings, but also provide a practical guideline to decide when co-training should work well based on datasets.",
	isbn="978-3-642-01307-2"
}


@InProceedings{search3,
	title = 	 {Efficient Neural Architecture Search via Parameters Sharing},
	author =       {Pham, Hieu and Guan, Melody and Zoph, Barret and Le, Quoc and Dean, Jeff},
	booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
	pages = 	 {4095--4104},
	year = 	 {2018},
	editor = 	 {Dy, Jennifer and Krause, Andreas},
	volume = 	 {80},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {10--15 Jul},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v80/pham18a/pham18a.pdf},
	url = 	 {https://proceedings.mlr.press/v80/pham18a.html},
	abstract = 	 {We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. ENAS constructs a large computational graph, where each subgraph represents a neural network architecture, hence forcing all architectures to share their parameters. A controller is trained with policy gradient to search for a subgraph that maximizes the expected reward on a validation set. Meanwhile a model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Sharing parameters among child models allows ENAS to deliver strong empirical performances, whilst using much fewer GPU-hours than existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On Penn Treebank, ENAS discovers a novel architecture that achieves a test perplexity of 56.3, on par with the existing state-of-the-art among all methods without post-training processing. On CIFAR-10, ENAS finds a novel architecture that achieves 2.89% test error, which is on par with the 2.65% test error of NASNet (Zoph et al., 2018).}
}


@article{search1,
	title = {Convolutional Neural Network Hyper-Parameters Optimization based on Genetic Algorithms},
	journal = {International Journal of Advanced Computer Science and Applications},
	doi = {10.14569/IJACSA.2018.091031},
	url = {http://dx.doi.org/10.14569/IJACSA.2018.091031},
	year = {2018},
	publisher = {The Science and Information Organization},
	volume = {9},
	number = {10},
	author = {Sehla Loussaief and Afef Abdelkrim}
} 

@inproceedings{mvada,
	title={An algorithm on multi-view adaboost},
	author={Xu, Zhijie and Sun, Shiliang},
	booktitle={International conference on Neural information processing},
	pages={355--362},
	year={2010},
	organization={Springer}
}

@article{mvsurvey,
	title={A survey of multi-view machine learning},
	author={Sun, Shiliang},
	journal={Neural computing and applications},
	volume={23},
	number={7},
	pages={2031--2038},
	year={2013},
	publisher={Springer}
}

@Article{gb_rev,
	author={Bent{\'e}jac, Candice
	and Cs{\"o}rg{\H{o}}, Anna
	and Mart{\'i}nez-Mu{\~{n}}oz, Gonzalo},
	title={A comparative analysis of gradient boosting algorithms},
	journal={Artificial Intelligence Review},
	year={2021},
	month={Mar},
	day={01},
	volume={54},
	number={3},
	pages={1937-1967},
	abstract={The family of gradient boosting algorithms has been recently extended with several interesting proposals (i.e. XGBoost, LightGBM and CatBoost) that focus on both speed and accuracy. XGBoost is a scalable ensemble technique that has demonstrated to be a reliable and efficient machine learning challenge solver. LightGBM is an accurate model focused on providing extremely fast training performance using selective sampling of high gradient instances. CatBoost modifies the computation of gradients to avoid the prediction shift in order to improve the accuracy of the model. This work proposes a practical analysis of how these novel variants of gradient boosting work in terms of training speed, generalization performance and hyper-parameter setup. In addition, a comprehensive comparison between XGBoost, LightGBM, CatBoost, random forests and gradient boosting has been performed using carefully tuned models as well as using their default settings. The results of this comparison indicate that CatBoost obtains the best results in generalization accuracy and AUC in the studied datasets although the differences are small. LightGBM is the fastest of all methods but not the most accurate. Finally, XGBoost places second both in accuracy and in training speed. Finally an extensive analysis of the effect of hyper-parameter tuning in XGBoost, LightGBM and CatBoost is carried out using two novel proposed tools.},
	issn={1573-7462},
	doi={10.1007/s10462-020-09896-5},
	url={https://doi.org/10.1007/s10462-020-09896-5}
}

@InProceedings{ensemble,
	author="Dietterich, Thomas G.",
	title="Ensemble Methods in Machine Learning",
	booktitle="Multiple Classifier Systems",
	year="2000",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="1--15",
	abstract="Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.",
	isbn="978-3-540-45014-6"
}

@article{margin,
	author = {Peter Bartlett and Yoav Freund and Wee Sun Lee and Robert E. Schapire},
	title = {{Boosting the margin: a new explanation for the effectiveness of voting methods}},
	volume = {26},
	journal = {The Annals of Statistics},
	number = {5},
	publisher = {Institute of Mathematical Statistics},
	pages = {1651 -- 1686},
	keywords = {bagging, boosting, decision trees, Ensemble methods, error-correcting, Markov chain, Monte Carlo, neural networks, output coding},
	year = {1998},
	doi = {10.1214/aos/1024691352},
	URL = {https://doi.org/10.1214/aos/1024691352}
}





@article{lasso1,
	author = {Tibshirani, Rob and Hastie, Trevor and Friedman, Jerome},
	year = {2010},
	month = {02},
	pages = {},
	title = {Regularized Paths for Generalized Linear Models Via Coordinate Descent},
	volume = {33},
	journal = {Journal of Statistical Software},
}
doi = {10.1163/ej.9789004178922.i-328.7}

@article{lasso2,
	author = {Koh, Kwangmoo and Kim, Seung-Jean and Boyd, Stephen},
	title = {An Interior-Point Method for Large-Scale L1-Regularized Logistic Regression},
	journal = {J. Mach. Learn. Res.},
	issue_date = {12/1/2007},
	volume = {8},
	month = {dec},
	year = {2007},
	issn = {1532-4435},
	pages = {1519--1555},
	numpages = {37},
	acmid = {1314550},
	publisher = {JMLR.org},
} 
url = {http://dl.acm.org/citation.cfm?id=1314498.1314550},



@Article{McAllester1999,
	author="McAllester, David A.",
	title="Some PAC-Bayesian Theorems",
	journal="Machine Learning",
	year="1999",
	month="Dec",
	day="01",
	volume="37",
	number="3",
	pages="355--363",
	abstract="This paper gives PAC guarantees for ``Bayesian'' algorithms---algorithms that optimize risk minimization expressions involving a prior probability and a likelihood for the training data. PAC-Bayesian algorithms are motivated by a desire to provide an informative prior encoding information about the expected experimental setting but still having PAC performance guarantees over all IID settings. The PAC-Bayesian theorems given here apply to an arbitrary prior measure on an arbitrary concept space. These theorems provide an alternative to the use of VC dimension in proving PAC bounds for parameterized concepts.",
	issn="1573-0565",doi="10.1023/A:1007618624809",
	url="https://doi.org/10.1023/A:1007618624809"
}


@Article{edge,
	author="Demiriz, Ayhan
	and Bennett, Kristin P.
	and Shawe-Taylor, John",
	title="Linear Programming Boosting via Column Generation",
	journal="Machine Learning",
	year="2002",
	month="Jan",
	day="01",
	volume="46",
	number="1",
	pages="225--254",
	abstract="We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as if all possible weak hypotheses had already been generated. The labels produced by the weak hypotheses become the new feature space of the problem. The boosting task becomes to construct a learning function in the label space that minimizes misclassification error and maximizes the soft margin. We prove that for classification, minimizing the 1-norm soft margin error function directly optimizes a generalization error bound. The equivalent linear program can be efficiently solved using column generation techniques developed for large-scale optimization problems. The resulting LPBoost algorithm can be used to solve any LP boosting formulation by iteratively optimizing the dual misclassification costs in a restricted LP and dynamically generating weak hypotheses to make new LP columns. We provide algorithms for soft margin classification, confidence-rated, and regression boosting problems. Unlike gradient boosting algorithms, which may converge in the limit only, LPBoost converges in a finite number of iterations to a global solution satisfying mathematically well-defined optimality conditions. The optimal solutions of LPBoost are very sparse in contrast with gradient based methods. Computationally, LPBoost is competitive in quality and computational cost to AdaBoost.",
	issn="1573-0565",
	doi="10.1023/A:1012470815092",
	url="https://doi.org/10.1023/A:1012470815092"
}


@article{adaboost,
	title = "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting",
	journal = "Jour. of Comp. and Sys. Sci.",
	volume = "55",
	number = "1",
	pages = "119-139",
	year = "1997",
	issn = "0022-0000",
	author = "Yoav Freund and Robert E Schapire",
	abstract = "In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update Littlestone–Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games, and prediction of points in Rn. In the second part of the paper we apply the multiplicative weight-update technique to derive a new boosting algorithm. This boosting algorithm does not require any prior knowledge about the performance of the weak learning algorithm. We also study generalizations of the new boosting algorithm to the problem of learning functions whose range, rather than being binary, is an arbitrary finite set or a bounded segment of the real line.",
	doi = "https://doi.org/10.1006/jcss.1997.1504",
	url = "http://www.sciencedirect.com/science/article/pii/S002200009791504X",
}


@Article{bagging,
	author="Breiman, Leo",
	title="Bagging Predictors",
	journal="Machine Learning",
	year="1996",
	month="Aug",
	day="01",
	volume="24",
	number="2",
	pages="123--140",
	abstract="Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.",
	issn="1573-0565",
	doi="10.1023/A:1018054314350",
	url="https://doi.org/10.1023/A:1018054314350"
}



@article{kmer,
	title={Sense from sequence reads: methods for alignment and assembly},
	author={Flicek, Paul and Birney, Ewan},
	journal={Nature methods},
	volume={6},
	number={11},
	pages={S6--S12},
	year={2009},
	publisher={Nature Publishing Group}
}


@misc{epigen, 
	title={Epigenomics fact sheet}, 
	url={https://www.genome.gov/about-genomics/fact-sheets/Epigenomics-Fact-Sheet}, 
	journal={Genome.gov}, 
	year={2022}} 


@incollection{prot,
	title = {Chapter 8 - Proteomics: The Deciphering of the Functional Genome},
	editor = {Geoffrey S. Ginsburg and Huntington F. Willard},
	booktitle = {Essentials of Genomic and Personalized Medicine},
	publisher = {Academic Press},
	address = {San Diego},
	pages = {89-96},
	year = {2010},
	isbn = {978-0-12-374934-5},
	doi = {https://doi.org/10.1016/B978-0-12-374934-5.00008-8},
	url = {https://www.sciencedirect.com/science/article/pii/B9780123749345000088},
	author = {Li-Rong Yu and Nicolas A. Stewart and Timothy D. Veenstra},
	abstract = {Publisher Summary
	Proteomics is the analysis of the entire protein complement of a cell, tissue, or organism under a specific, defined set of conditions. In its present state, it is dependent on decades of technological and instrumental developments. These developments have included advances in mass spectrometry (MS) technology, protein fractionation techniques, bioinformatics, etc. Proteomics relies on three basic technological cornerstones that include a method to fractionate complex protein or peptide mixtures, MS to acquire the data necessary to identify individual proteins, and bioinformatics to analyze and assemble the MS data. While MS and bioinformatic components are somewhat similar in most applications, there are two distinct methods to separate complex protein samples in proteomics. This separation technology is the most common separation technique used in proteomics today. Advances in this technology have enabled thousands of proteins to be resolved in a single gel. No area of proteomics has evolved faster than bioinformatics. Proteomics is critically dependent on bioinformatics to process the raw mass spectral data into protein data. While routinely used by every laboratory, the most critical software programs are those that take peptide mapping and/or tandem MS results and determine the protein or peptide sequence that most closely matches the experimental data.}
}

@incollection{metabo,
	title = {Chapter Two - Metabolomics: Strategies to Define the Role of Metabolism in Virus Infection and Pathogenesis},
	editor = {Margaret Kielian and Thomas C. Mettenleiter and Marilyn J. Roossinck},
	series = {Advances in Virus Research},
	publisher = {Academic Press},
	volume = {98},
	pages = {57-81},
	year = {2017},
	issn = {0065-3527},
	doi = {https://doi.org/10.1016/bs.aivir.2017.02.001},
	url = {https://www.sciencedirect.com/science/article/pii/S0065352717300015},
	author = {Marianne Manchester and Anisha Anand},
	keywords = {Virus, Metabolomics, LC-MS, GC-MS, Systems biology, Metabolism, Metabolite, Network, Metabolic flux, Mass spectrometry, NMR},
	abstract = {Metabolomics is an analytical profiling technique for measuring and comparing large numbers of metabolites present in biological samples. Combining high-throughput analytical chemistry and multivariate data analysis, metabolomics offers a window on metabolic mechanisms. Because they intimately utilize and often rewire host metabolism, viruses are an excellent choice to study by metabolomics techniques. Studies of the effects of viruses on metabolism during replication in vitro and infection in animal models or human subjects have provided novel insights into these networks and provided new targets for therapy and biomarker development. Identifying the common metabolic pathways utilized by viruses has the potential to reveal those that can be targeted by broad-spectrum antiviral and vaccine approaches.}
}

@incollection{trans,
	title = {Transcriptomics},
	editor = {Ralph A. Bradshaw and Philip D. Stahl},
	booktitle = {Encyclopedia of Cell Biology},
	publisher = {Academic Press},
	address = {Waltham},
	pages = {160-165},
	year = {2016},
	isbn = {978-0-12-394796-3},
	doi = {https://doi.org/10.1016/B978-0-12-394447-4.40029-5},
	url = {https://www.sciencedirect.com/science/article/pii/B9780123944474400295},
	author = {E.A. Milward and A. Shahandeh and M. Heidari and D.M. Johnstone and N. Daneshi and H. Hondermarck},
	keywords = {Genomics, High-throughput, Microarray, Nanopore, Noncoding RNA, Omics, Proteogenomics, Proteomics, RNA-seq, Transcriptomics},
	abstract = {The field of transcriptomics seeks to understand all aspects of RNA (ribonucleic acid) biology. This encompasses knowledge about RNA sequences, structures, transcription, translation, and functions. This article presents an overview of the rapid evolution of transcriptomics driven by the emergence of new, ultrarapid macromolecular sequencing and other high-throughput technologies, and how this is influencing perceptions of biological and pathobiological systems.}
}

@ARTICLE{transcriptome,
	title     = "The Genexpress {IMAGE} knowledge base of the human brain
	transcriptome: a prototype integrated resource for functional
	and computational genomics",
	author    = "Pi{\'e}tu, G and Mariage-Samson, R and Fayein, N A and Matingou,
	C and Eveno, E and Houlgatte, R and Decraene, C and
	Vandenbrouck, Y and Tahi, F and Devignes, M D and Wirkner, U and
	Ansorge, W and Cox, D and Nagase, T and Nomura, N and Auffray, C",
	abstract  = "Expression profiles of 5058 human gene transcripts represented
	by an array of 7451 clones from the first IMAGE Consortium cDNA
	library from infant brain have been collected by
	semiquantitative hybridization of the array with complex probes
	derived by reverse transcription of mRNA from brain and five
	other human tissues. Twenty-one percent of the clones
	corresponded to transcripts that could be classified in general
	categories of low, moderate, or high abundance. These expression
	profiles were integrated with cDNA clone and sequence clustering
	and gene mapping information from an upgraded version of the
	Genexpress Index. For seven gene transcripts found to be
	transcribed preferentially or specifically in brain, the
	expression profiles were confirmed by Northern blot analyses of
	mRNA from eight adult and four fetal tissues, and 15 distinct
	regions of brain. In four instances, further documentation of
	the sites of expression was obtained by in situ hybridization of
	rat-brain tissue sections. A systematic effort was undertaken to
	further integrate available cytogenetic, genetic, physical, and
	genic map informations through radiation-hybrid mapping to
	provide a unique validated map location for each of these genes
	in relation to the disease map. The resulting Genexpress IMAGE
	Knowledge Base is illustrated by five examples presented in the
	printed article with additional data available on a dedicated
	Web site at the address http://idefix.upr420.vjf.cnrs.fr/EXPR++
	+/ welcome.html.",
	journal   = "Genome Res.",
	publisher = "Cold Spring Harbor Laboratory",
	volume    =  9,
	number    =  2,
	pages     = "195--209",
	month     =  feb,
	year      =  1999,
	language  = "en"
}

@article{multi-omics,
	title = {Integration strategies of multi-omics data for machine learning analysis},
	journal = {Computational and Structural Biotechnology Journal},
	volume = {19},
	pages = {3735-3746},
	year = {2021},
	issn = {2001-0370},
	doi = {https://doi.org/10.1016/j.csbj.2021.06.030},
	url = {https://www.sciencedirect.com/science/article/pii/S2001037021002683},
	author = {Milan Picard and Marie-Pier Scott-Boyer and Antoine Bodein and Olivier Périn and Arnaud Droit},
	keywords = {Multi-omics, Multi-view, Integration strategy, Machine learning, Deep learning, Network},
	abstract = {Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.}
}

@Article{kover,
	author={Drouin, Alexandre
	and Gigu{\`e}re, S{\'e}bastien
	and D{\'e}raspe, Maxime
	and Marchand, Mario
	and Tyers, Michael
	and Loo, Vivian G.
	and Bourgault, Anne-Marie
	and Laviolette, Fran{\c{c}}ois
	and Corbeil, Jacques},
	title={Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons},
	journal={BMC Genomics},
	year={2016},
	month={Sep},
	day={26},
	volume={17},
	number={1},
	pages={754},
	abstract={The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies.},
	issn={1471-2164},
	doi={10.1186/s12864-016-2889-6},
	url={https://doi.org/10.1186/s12864-016-2889-6}
}

@ARTICLE{DNAlength,
	title     = "On the length, weight and {GC} content of the human genome",
	author    = "Piovesan, Allison and Pelleri, Maria Chiara and Antonaros,
	Francesca and Strippoli, Pierluigi and Caracausi, Maria and
	Vitale, Lorenza",
	abstract  = "OBJECTIVE: Basic parameters commonly used to describe genomes
	including length, weight and relative guanine-cytosine (GC)
	content are widely cited in absence of a primary source. By
	using updated data and original software we determined these
	values to the best of our knowledge as standard reference for
	the whole human nuclear genome, for each chromosome and for
	mitochondrial DNA. We also devised a method to calculate the
	relative GC content in the whole messenger RNA sequence set and
	in transcriptomes by multiplying the GC content of each gene by
	its mean expression level. RESULTS: The male nuclear diploid
	genome extends for 6.27 Gigabase pairs (Gbp), is 205.00 cm (cm)
	long and weighs 6.41 picograms (pg). Female values are 6.37 Gbp,
	208.23 cm, 6.51 pg. The individual variability and the
	implication for the DNA informational density in terms of
	bits/volume were discussed. The genomic GC content is 40.9\%.
	Following analysis in different transcriptomes and species, we
	showed that the greatest deviation was observed in the
	pathological condition analysed (trisomy 21 leukaemic cells) and
	in Caenorhabditis elegans. Our results may represent a solid
	basis for further investigation on human structural and
	functional genomics while also providing a framework for other
	genome comparative analysis.",
	journal   = "BMC Res. Notes",
	publisher = "Springer Science and Business Media LLC",
	volume    =  12,
	number    =  1,
	pages     = "106",
	month     =  feb,
	year      =  2019,
	keywords  = "GC content; Genome length; Genome weight; Human genome;
	Mitochondrial DNA",
	language  = "en"
}

@article{symptoms,
	author = {Olalekan Lee Aiyegbusi and Sarah E Hughes and Grace Turner and Samantha Cruz Rivera and Christel McMullan and Joht Singh Chandan and Shamil Haroon and Gary Price and Elin Haf Davies and Krishnarajah Nirantharakumar and Elizabeth Sapey and Melanie J Calvert and on behalf of the TLC Study Group},
	title ={Symptoms, complications and management of long COVID: a review},
	journal = {Journal of the Royal Society of Medicine},
	volume = {114},
	number = {9},
	pages = {428-442},
	year = {2021},
	doi = {10.1177/01410768211032850},
	note ={PMID: 34265229},
	
	URL = { 
	https://doi.org/10.1177/01410768211032850
	
	},
	eprint = { https://doi.org/10.1177/01410768211032850
	}
	,
	abstract = { Globally, there are now over 160 million confirmed cases of COVID-19 and more than 3 million deaths. While the majority of infected individuals recover, a significant proportion continue to experience symptoms and complications after their acute illness. Patients with ‘long COVID’ experience a wide range of physical and mental/psychological symptoms. Pooled prevalence data showed the 10 most prevalent reported symptoms were fatigue, shortness of breath, muscle pain, joint pain, headache, cough, chest pain, altered smell, altered taste and diarrhoea. Other common symptoms were cognitive impairment, memory loss, anxiety and sleep disorders. Beyond symptoms and complications, people with long COVID often reported impaired quality of life, mental health and employment issues. These individuals may require multidisciplinary care involving the long-term monitoring of symptoms, to identify potential complications, physical rehabilitation, mental health and social services support. Resilient healthcare systems are needed to ensure efficient and effective responses to future health challenges. }
}



@Article{kover2,
	author={Drouin, Alexandre
	and Letarte, Ga{\"e}l
	and Raymond, Fr{\'e}d{\'e}ric
	and Marchand, Mario
	and Corbeil, Jacques
	and Laviolette, Fran{\c{c}}ois},
	title={Interpretable genotype-to-phenotype classifiers with performance guarantees},
	journal={Scientific Reports},
	year={2019},
	month={Mar},
	day={11},
	volume={9},
	number={1},
	pages={4071},
	abstract={Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use in this setting. The high dimensionality of the data tends to hinder generalization and challenges the scalability of most learning algorithms. Additionally, most algorithms produce models that are complex and difficult to interpret. We alleviate these limitations by proposing strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models. We show that these guarantees can be leveraged to accelerate learning and improve model interpretability. Our approach is validated through an application to the genomic prediction of antimicrobial resistance, an important public health concern. Highly accurate models were obtained for 12 species and 56 antibiotics, and their interpretation revealed known resistance mechanisms, as well as some potentially new ones. An open-source disk-based implementation that is both memory and computationally efficient is provided with this work. The implementation is turnkey, requires no prior knowledge of machine learning, and is complemented by comprehensive tutorials.},
	issn={2045-2322},
	doi={10.1038/s41598-019-40561-2},
	url={https://doi.org/10.1038/s41598-019-40561-2}
}


@inproceedings{goyal2017pac,
	title={Pac-bayesian analysis for a two-step hierarchical multiview learning approach},
	author={Goyal, Anil and Morvant, Emilie and Germain, Pascal and Amini, Massih-Reza},
	booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	pages={205--221},
	year={2017},
	organization={Springer}
}


@inproceedings{mvm,
	author = {Cao, Bokai and Zhou, Hucheng and Li, Guoqiang and Yu, Philip S.},
	title = {Multi-View Machines},
	year = {2016},
	isbn = {9781450337168},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2835776.2835777},
	doi = {10.1145/2835776.2835777},
	abstract = {With rapidly growing amount of data available on the web, it becomes increasingly likely to obtain data from different perspectives for multi-view learning. Some successive examples of web applications include recommendation and target advertising. Specifically, to predict whether a user will click an ad in a query context, there are available features extracted from user profile, ad information and query description, and each of them can only capture part of the task signals from a particular aspect/view. Different views provide complementary information to learn a practical model for these applications. Therefore, an effective integration of the multi-view information is critical to facilitate the learning performance.In this paper, we propose a general predictor, named multi-view machines (MVMs), that can effectively explore the full-order interactions between features from multiple views. A joint factorization is applied for the interaction parameters which makes parameter estimation more accurate under sparsity and renders the model with the capacity to avoid overfitting. Moreover, MVMs can work in conjunction with different loss functions for a variety of machine learning tasks. The advantages of MVMs are illustrated through comparison with other methods for multi-view prediction, including support vector machines (SVMs), support tensor machines (STMs) and factorization machines (FMs).A stochastic gradient descent method and a distributed implementation on Spark are presented to learn the MVM model. Through empirical studies on two real-world web application datasets, we demonstrate the effectiveness of MVMs on modeling feature interactions in multi-view data. A 3.51% accuracy improvement is shown on MVMs over FMs for the problem of movie rating prediction, and 0.57% for ad click prediction.},
	booktitle = {Proceedings of the Ninth ACM International Conference on Web Search and Data Mining},
	pages = {427–436},
	numpages = {10},
	keywords = {feature interaction, multi-view learning, factorization},
	location = {San Francisco, California, USA},
	series = {WSDM '16}
}


@Article{cbboost,
	author={Bauvin, Baptiste
	and Capponi, C{\'e}cile
	and Roy, Jean-Francis
	and Laviolette, Fran{\c{c}}ois},
	title={Fast greedy C-bound minimization with guarantees},
	journal={Machine Learning},
	year={2020},
	month={Sep},
	day={01},
	volume={109},
	number={9},
	pages={1945-1986},
	abstract={The {\$}{\$}{\backslash}mathcal {\{}C{\}}{\$}{\$}-bound is a tight bound on the true risk of a majority vote classifier that relies on the individual quality and pairwise disagreement of the voters and provides PAC-Bayesian generalization guarantees. Based on this bound, MinCq is a classification algorithm that returns a dense distribution on a finite set of voters by minimizing it. Introduced later and inspired by boosting, CqBoost uses a column generation approach to build a sparse {\$}{\$}{\backslash}mathcal {\{}C{\}}{\$}{\$}-bound optimal distribution on a possibly infinite set of voters. However, both approaches have a high computational learning time because they minimize the {\$}{\$}{\backslash}mathcal {\{}C{\}}{\$}{\$}-bound by solving a quadratic program. Yet, one advantage of CqBoost is its experimental ability to provide sparse solutions. In this work, we address the problem of accelerating the {\$}{\$}{\backslash}mathcal {\{}C{\}}{\$}{\$}-bound minimization process while keeping the sparsity of the solution and without losing accuracy. We present CB-Boost, a computationally efficient classification algorithm relying on a greedy--boosting-based--{\$}{\$}{\backslash}mathcal {\{}C{\}}{\$}{\$}-bound optimization. An in-depth analysis proves the optimality of the greedy minimization process and quantifies the decrease of the {\$}{\$}{\backslash}mathcal {\{}C{\}}{\$}{\$}-bound operated by the algorithm. Generalization guarantees are then drawn based on already existing PAC-Bayesian theorems. In addition, we experimentally evaluate the relevance of CB-Boost in terms of the three main properties we expect about it: accuracy, sparsity, and computational efficiency compared to MinCq, CqBoost, Adaboost and other ensemble methods. As observed in these experiments, CB-Boost not only achieves results comparable to the state of the art, but also provides {\$}{\$}{\backslash}mathcal {\{}C{\}}{\$}{\$}-bound sub-optimal weights with very few computational demand while keeping the sparsity property of CqBoost.},
	issn={1573-0565},
	doi={10.1007/s10994-020-05902-7},
	url={https://doi.org/10.1007/s10994-020-05902-7}
}



@article{shapth,
	title = "Boosting the margin: A new explanation for the effectiveness of voting methods",
	abstract = "One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.",
	keywords = "Bagging, Boosting, Decision trees, Ensemble methods, Error-correcting, Markov chain, Monte Carlo, Neural networks, Output coding",
	author = "Schapire, {Robert E.} and Yoav Freund and Peter Bartlett and Lee, {Wee Sun}",
	year = "1998",
	month = oct,
	doi = "10.1214/aos/1024691352",
	language = "English (US)",
	volume = "26",
	pages = "1651--1686",
	journal = "Annals of Statistics",
	issn = "0090-5364",
	publisher = "Institute of Mathematical Statistics",
	number = "5",	
}


@article{edgeoverrandom,
	author = {Mukherjee, Indraneel and Schapire, Robert E.},
	title = {A Theory of Multiclass Boosting},
	year = {2013},
	issue_date = {January 2013},
	publisher = {JMLR.org},
	volume = {14},
	number = {1},
	issn = {1532-4435},
	abstract = {Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.},
	journal = {J. Mach. Learn. Res.},
	month = {feb},
	pages = {437–497},
	numpages = {61},
	keywords = {weak learning condition, boosting, multiclass, drifting games}
}

@misc{plotly, 
	author = {Plotly Technologies Inc.}, 
	title = {Collaborative data science}, 
	publisher = {Plotly Technologies Inc.}, 
	address = {Montreal, QC}, 
	year = {2015}, 
	url = {https://plot.ly}
}

@misc{code_trend,
	author       = {Volker Strobel},
	title        = {Pold87/academic-keyword-occurrence: First release},
	month        = apr,
	year         = 2018,
	publisher    = {Zenodo},
	version      = {v1.0.0},
	doi          = {10.5281/zenodo.1218409},
	url          = {https://doi.org/10.5281/zenodo.1218409}
}

@article{interpro,
	Title = {InterPro in 2022},
	Author = {Paysan-Lafosse, Typhaine and Blum, Matthias and Chuguransky, Sara and Grego, Tiago and Pinto, Beatriz Lázaro and Salazar, Gustavo A and Bileschi, Maxwell L and Bork, Peer and Bridge, Alan and Colwell, Lucy and Gough, Julian and Haft, Daniel H and Letunić, Ivica and Marchler-Bauer, Aron and Mi, Huaiyu and Natale, Darren A and Orengo, Christine A and Pandurangan, Arun P and Rivoire, Catherine and Sigrist, Christian J A and Sillitoe, Ian and Thanki, Narmada and Thomas, Paul D and Tosatto, Silvio C E and Wu, Cathy H and Bateman, Alex},
	DOI = {10.1093/nar/gkac993},
	Month = {November},
	Year = {2022},
	Journal = {Nucleic acids research},
	ISSN = {0305-1048},
	Pages = {gkac993},
	Abstract = {The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. Here, we report recent developments with InterPro (version 90.0) and its associated software, including updates to data content and to the website. These developments extend and enrich the information provided by InterPro, and provide a more user friendly access to the data. Additionally, we have worked on adding Pfam website features to the InterPro website, as the Pfam website will be retired in late 2022. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB. Moreover, we report the development of a card game as a method of engaging the non-scientific community. Finally, we discuss the benefits and challenges brought by the use of artificial intelligence for protein structure prediction.},
	URL = {https://doi.org/10.1093/nar/gkac993},
}

@misc{poutyne,
	author = {Paradis, Frédérik and Beauchemin, David and Godbout, Mathieu and Alain, Mathieu and Garneau, Nicolas and Otte, Stefan and Tremblay, Alexis and Bélanger, Marc-Antoine and Laviolette, François},
	title  = {{Poutyne: A Simplified Framework for Deep Learning}},
	year   = {2020},
	url    = {https://poutyne.org}
}

@article{multimodal,
	author  = {Dominique Benielli and Baptiste Bauvin and Sokol Koço and Riikka Huusari and Cécile Capponi and Hachem Kadri and François Laviolette},
	title   = {Toolbox for Multimodal Learn (scikit-multimodallearn)},
	journal = {Journal of Machine Learning Research},
	year    = {2022},
	volume  = {23},
	number  = {51},
	pages   = {1--7},
	url     = {http://jmlr.org/papers/v23/21-0791.html}
}

@inproceedings{mage,
	TITLE = {{Multi-view Artificial Generation Engine: MAGE -- Controlled data generator for multi-view learning}},
	AUTHOR = {Bauvin, Baptiste and Ko{\c c}o, Sokol and Benielli, Dominique and Capponi, Cecile and Laviolette, Fran{\c c}ois},
	URL = {https://hal.archives-ouvertes.fr/hal-03524726},
	BOOKTITLE = {{CAp 2021 - Conf{\'e}rence sur l'Aprentissage Auomatique}},
	ADDRESS = {Saint-Etienne, France},
	YEAR = {2021},
	MONTH = Jun,
	KEYWORDS = {Multi-View ; Classification ; Generation ; Probabilistic ; Supervised},
	PDF = {https://hal.archives-ouvertes.fr/hal-03524726/file/Multiview_generator.pdf},
	HAL_ID = {hal-03524726},
	HAL_VERSION = {v1},
}

@InProceedings{sokol13,
	title = 	 {On multi-class classification through the minimization of the confusion matrix norm},
	author = 	 {Koço, Sokol and Capponi, Cécile},
	booktitle = 	 {Proceedings of the 5th Asian Conference on Machine Learning},
	pages = 	 {277--292},
	year = 	 {2013},
	editor = 	 {Ong, Cheng Soon and Ho, Tu Bao},
	volume = 	 {29},
	series = 	 {Proceedings of Machine Learning Research},
	address = 	 {Australian National University, Canberra, Australia},
	month = 	 {13--15 Nov},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v29/Koco13.pdf},
	url = 	 {https://proceedings.mlr.press/v29/Koco13.html},
	abstract = 	 {In imbalanced multi-class classification problems, the misclassification rate as an error measure may not be a relevant choice. Several methods have been developed where the performance measure retained richer information than the mere misclassification rate: misclassification costs, ROC-based information, etc. Following this idea of dealing with alternate measures of performance, we propose to address imbalanced classification problems by using a new measure to be optimized: the norm of the confusion matrix. Indeed, recent results show that using the norm of the confusion matrix as an error measure can be quite interesting due to the fine-grain informations contained in the matrix,  especially in the case of imbalanced classes. Our first contribution then consists in showing that optimizing criterion based on the confusion matrix gives rise to a common background for cost-sensitive methods aimed at dealing with imbalanced classes learning problems. As our second contribution, we propose an extension of a recent multi-class boosting method — namely AdaBoost.MM — to the imbalanced class problem, by greedily minimizing  the empirical norm of the confusion matrix. A theoretical analysis of the properties of the proposed method is presented, while experimental results illustrate the behavior of the algorithm and show the relevancy of the approach compared to other methods.}
}


@InProceedings{Huu2019,
	title = 	 {Multi-view Metric Learning in Vector-valued Kernel Spaces},
	author = 	 {Huusari, Riikka and Kadri, Hachem and Capponi, Cécile},
	booktitle = 	 {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
	pages = 	 {415--424},
	year = 	 {2018},
	editor = 	 {Storkey, Amos and Perez-Cruz, Fernando},
	volume = 	 {84},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {09--11 Apr},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v84/huusari18a/huusari18a.pdf},
	url = 	 {https://proceedings.mlr.press/v84/huusari18a.html},
	abstract = 	 {We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, as a way to capture multi-modal structure of the data. We formulate two convex optimization problems to jointly learn the metric and the classifier or regressor in kernel feature spaces. An iterative three-step multi-view metric learning algorithm is derived from the optimization problems. In order to scale the computation to large training sets, a block-wise Nyström approximation of the multi-view kernel matrix is introduced. We justify our approach theoretically and experimentally, and show its performance on real-world datasets against relevant state-of-the-art methods.}
}



@article{sharedboost,
	title = {Boosting for transfer learning from multiple data sources},
	journal = {Pattern Recognition Letters},
	volume = {33},
	number = {5},
	pages = {568-579},
	year = {2012},
	issn = {0167-8655},
	doi = {https://doi.org/10.1016/j.patrec.2011.11.023},
	url = {https://www.sciencedirect.com/science/article/pii/S0167865511004132},
	author = {Pipei Huang and Gang Wang and Shiyin Qin},
	keywords = {Opinion mining, Sentimental classification, Boosting, Transfer learning, Transfer learning with multiple sources, Multiple source domains},
	abstract = {Transfer learning aims at adapting a classifier trained on one domain with adequate labeled samples to a new domain where samples are from a different distribution and have no class labels. In this paper, we explore the transfer learning problems with multiple data sources and present a novel boosting algorithm, SharedBoost. This novel algorithm is capable of applying for very high dimensional data such as in text mining where the feature dimension is beyond several ten thousands. The experimental results illustrate that the SharedBoost algorithm significantly outperforms the traditional methods which transfer knowledge with supervised learning techniques. Besides, SharedBoost also provides much better classification accuracy and more stable performance than some other typical transfer learning methods such as the structural correspondence learning (SCL) and the structural learning in the multiple sources transfer learning problems.}
}



@misc{lives_dataset,
	author       = {Prodhomme, Alexis and Benielli, Dominique and Bauvin, Baptiste and Capponi, Cecile },
	title        = {LIVES Dataset},
	month        = Feb,
	year         = 2020,
	publisher    = {Gitlab},
	version      = {v1.0.0},
	url          = {https://gitlab.lis-lab.fr/dev/lives_dataset}
}

@inproceedings{mv3d,
	author = {Utasi, Ákos and Benedek, Csaba},
	year = {2012},
	month = {05},
	pages = {},
	title = {A Multi-View Annotation Tool for People Detection Evaluation},
	doi = {10.1145/2304496.2304499}
}

@ARTICLE{videomv,
	
	author={Jiang, Yu-Gang and Wu, Zuxuan and Tang, Jinhui and Li, Zechao and Xue, Xiangyang and Chang, Shih-Fu},
	
	journal={IEEE Transactions on Multimedia}, 
	
	title={Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification}, 
	
	year={2018},
	
	volume={20},
	
	number={11},
	
	pages={3137-3147},
	
	doi={10.1109/TMM.2018.2823900}}


@book{ROI,
	title={The Art and Science of Digital Compositing: Techniques for Visual Effects, Animation and Motion Graphics},
	author={Brinkmann, R.},
	isbn={9780123706386},
	lccn={2007053015},
	series={Morgan Kaufmann Series in Computer Graphics and Geometric Modeling},
	url={https://books.google.ca/books?id=yeMdlAEACAAJ},
	year={2008},
	publisher={Morgan Kaufmann Publishers/Elsevier}
}

@article{hog,
	title = {Method of and apparatus for pattern recognition},
	author = {McConnell, R K},
	abstractNote = {This patent describes a method of recognizing a pattern in a test object. The method consists of: specifying properties characteristic of the pattern; specifying discrete ranges of values of the properties; measuring the values of the properties in the test object; arranging the measured values in at least one test histogram; determining a reference set of values of the properties and arranging the set as at least a first reference histogram; and comparing the test and reference histograms by determination of the value of a function which provides a measure of the amount of information necessary to express the at least one test histogram in terms of the optimum code for describing at least the first reference histogram.},
	doi = {},
	url = {https://www.osti.gov/biblio/6007283},
	journal = {},
	number = {},
	volume = {},
	place = {United States},
	year = {1986},
	month = {1}
} 


@article{ICMV,
	abstract = {Classifiers can be combined to reduce classification errors. We did experiments on a data set consisting of different sets of features of handwritten digits. Different types of classifiers were trained on these feature sets. The performances of these classifiers and combination rules were tested. The best results were acquired with the mean, median and product combination rules. The product was best for combining linear classifiers, the median for $k$-NN classifiers. Training a classifier on all features did not result in less errors.},
	author = {Breukelen, M. and Duin, Robert P. W. and Tax, David M. J. and Hartog, J. E. den},
	journal = {Kybernetika},
	keywords = {handwritten digits; combined classifiers; handwritten digits; combined classifiers},
	language = {eng},
	number = {4},
	pages = {[381]-386},
	publisher = {Institute of Information Theory and Automation AS CR},
	title = {Handwritten digit recognition by combined classifiers},
	url = {http://eudml.org/doc/33365},
	volume = {34},
	year = {1998},
}


@inproceedings{lstm,
	author = {Hochreiter, Sepp and Schmidhuber, J\"{u}rgen},
	title = {LSTM Can Solve Hard Long Time Lag Problems},
	year = {1996},
	publisher = {MIT Press},
	address = {Cambridge, MA, USA},
	abstract = {Standard recurrent nets cannot deal with long minimal time lags between relevant signals. Several recent NIPS papers propose alternative methods. We first show: problems used to promote various previous algorithms can be solved more quickly by random weight guessing than by the proposed algorithms. We then use LSTM, our own recent algorithm, to solve a hard problem that can neither be quickly solved by random search nor by any other recurrent net algorithm we are aware of.},
	booktitle = {Proceedings of the 9th International Conference on Neural Information Processing Systems},
	pages = {473–479},
	numpages = {7},
	location = {Denver, Colorado},
	series = {NIPS'96}
}


@ARTICLE{difficulty,
	
	author={Hansen, L.K. and Salamon, P.},
	
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
	
	title={Neural network ensembles}, 
	
	year={1990},
	
	volume={12},
	
	number={10},
	
	pages={993-1001},
	
	doi={10.1109/34.58871}}


@article{scm_app,
	title={Learning interpretable models of phenotypes from whole genome sequences with the set covering machine},
	author={Drouin, Alexandre and Giguere, S{\'e}bastien and Sagatovich, Vladana and D{\'e}raspe, Maxime and Laviolette, Fran{\c{c}}ois and Marchand, Mario and Corbeil, Jacques},
	journal={arXiv preprint arXiv:1412.1074},
	year={2014}
}

@article{deepccs,
	title={Predicting ion mobility collision cross-sections using a deep neural network: DeepCCS},
	author={Plante, Pier-Luc and Francovic-Fontaine, Élina and May, Jody C and McLean, John A and Baker, Erin S and Laviolette, Fran{\c{c}}ois and Marchand, Mario and Corbeil, Jacques},
	journal={Analytical chemistry},
	volume={91},
	number={8},
	pages={5191--5199},
	year={2019},
	publisher={ACS Publications}
}

@inproceedings{GSCM,
	author = {Osseni, Mazid and Tossou, Prudencio and Corbeil, Jacques and Laviolette, Francois},
	year = {2021},
	month = {01},
	pages = {72-82},
	title = {Applying PySCMGroup to Breast Cancer Biomarkers Discovery},
	doi = {10.5220/0010375500720082}
}

@article{mazidbiomark,
	title={Machine learning analysis identifies genes differentiating triple negative breast cancers},
	author={Kothari, Charu and Osseni, Mazid Abiodoun and Agbo, Lynda and Ouellette, Genevi{\`e}ve and D{\'e}raspe, Maxime and Laviolette, Fran{\c{c}}ois and Corbeil, Jacques and Lambert, Jean-Philippe and Diorio, Caroline and Durocher, Francine},
	journal={Scientific reports},
	volume={10},
	number={1},
	pages={1--15},
	year={2020},
	publisher={Nature Publishing Group}
}

@ARTICLE{kegg,
  title     = "{KEGG}: kyoto encyclopedia of genes and genomes",
  author    = "Kanehisa, M and Goto, S",
  abstract  = "KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge
               base for systematic analysis of gene functions, linking genomic
               information with higher order functional information. The
               genomic information is stored in the GENES database, which is a
               collection of gene catalogs for all the completely sequenced
               genomes and some partial genomes with up-to-date annotation of
               gene functions. The higher order functional information is
               stored in the PATHWAY database, which contains graphical
               representations of cellular processes, such as metabolism,
               membrane transport, signal transduction and cell cycle. The
               PATHWAY database is supplemented by a set of ortholog group
               tables for the information about conserved subpathways (pathway
               motifs), which are often encoded by positionally coupled genes
               on the chromosome and which are especially useful in predicting
               gene functions. A third database in KEGG is LIGAND for the
               information about chemical compounds, enzyme molecules and
               enzymatic reactions. KEGG provides Java graphics tools for
               browsing genome maps, comparing two genome maps and manipulating
               expression maps, as well as computational tools for sequence
               comparison, graph comparison and path computation. The KEGG
               databases are daily updated and made freely available
               (http://www. genome.ad.jp/kegg/).",
  journal   = "Nucleic Acids Res.",
  publisher = "Oxford University Press (OUP)",
  volume    =  28,
  number    =  1,
  pages     = "27--30",
  month     =  jan,
  year      =  2000,
  language  = "en"
}


@article{rudininterpret,
	author    = {Cynthia Rudin and
	Chaofan Chen and
	Zhi Chen and
	Haiyang Huang and
	Lesia Semenova and
	Chudi Zhong},
	title     = {Interpretable Machine Learning: Fundamental Principles and 10 Grand
	Challenges},
	journal   = {CoRR},
	volume    = {abs/2103.11251},
	year      = {2021},
	url       = {https://arxiv.org/abs/2103.11251},
	eprinttype = {arXiv},
	eprint    = {2103.11251},
	timestamp = {Mon, 14 Nov 2022 08:07:36 +0100},
	biburl    = {https://dblp.org/rec/journals/corr/abs-2103-11251.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@book{interpretableml,
	title      = {Interpretable Machine Learning},
	author     = {Christoph Molnar},
	year       = {2022},
	subtitle   = {A Guide for Making Black Box Models Explainable},
	edition    = {2},
	url        = {https://christophm.github.io/interpretable-ml-book}
}


@Article{100gen,
	author={Auton, Adam
	and Abecasis, Gon{\c{c}}alo R.
	and The 1000 Genomes Project Consortium},
	title={A global reference for human genetic variation},
	journal={Nature},
	year={2015},
	month={Oct},
	day={01},
	volume={526},
	number={7571},
	pages={68-74},
	abstract={The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99{\%} of SNP variants with a frequency of >1{\%} for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.},
	issn={1476-4687},
	doi={10.1038/nature15393},
	url={https://doi.org/10.1038/nature15393}
}



@article{randomforest,
	author = {Breiman, Leo},
	title = {Random Forests},
	year = {2001},
	issue_date = {October 1 2001},
	publisher = {Kluwer Academic Publishers},
	address = {USA},
	volume = {45},
	number = {1},
	issn = {0885-6125},
	url = {https://doi.org/10.1023/A:1010933404324},
	doi = {10.1023/A:1010933404324},
	abstract = {Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund &amp; R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.},
	journal = {Mach. Learn.},
	month = oct,
	pages = {5–32},
	numpages = {28},
	keywords = {regression, classification, ensemble}
}

@article{pca,
	author = { Karl   Pearson},
	title = {LIII. On lines and planes of closest fit to systems of points in space},
	journal = {The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science},
	volume = {2},
	number = {11},
	pages = {559-572},
	year  = {1901},
	publisher = {Taylor & Francis},
	doi = {10.1080/14786440109462720},
} 

@article{deepgenom,
	title={Deep learning: new computational modelling techniques for genomics},
	author={Eraslan, G{\"o}kcen and Avsec, {\v{Z}}iga and Gagneur, Julien and Theis, Fabian J},
	journal={Nature Reviews Genetics},
	volume={20},
	number={7},
	pages={389--403},
	year={2019},
	publisher={Nature Publishing Group}
}

@inproceedings{deng2009imagenet,
	title={Imagenet: A large-scale hierarchical image database},
	author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
	booktitle={2009 IEEE conference on computer vision and pattern recognition},
	pages={248--255},
	year={2009},
	organization={Ieee}
}

@article{vrialescape,
	title={Learning the language of viral evolution and escape},
	author={Hie, Brian and Zhong, Ellen D and Berger, Bonnie and Bryson, Bryan},
	journal={Science},
	volume={371},
	number={6526},
	pages={284--288},
	year={2021},
	publisher={American Association for the Advancement of Science}
}

@article{dietnet,
	title={Diet networks: thin parameters for fat genomics},
	author={Romero, Adriana and Carrier, Pierre Luc and Erraqabi, Akram and Sylvain, Tristan and Auvolat, Alex and Dejoie, Etienne and Legault, Marc-Andr{\'e} and Dub{\'e}, Marie-Pierre and Hussin, Julie G and Bengio, Yoshua},
	journal={arXiv preprint arXiv:1611.09340},
	year={2016}
}

@article{med_na_dataset,
	title = {Prédiction de l’observance à court terme d’une diète méditerranéenne grâce à une approche métabolomique non ciblée et des études d’alimentation contrôlées},
	journal = {Nutrition Clinique et Métabolisme},
	year = {2020},
	url = {https://www.sciencedirect.com/science/article/pii/S0985056220304386},
	author = {D. Brassard and F. Brière and N. Boucher and P.-L. Plante and J. Corbeil and S. Lemieux and  B. Lamarche}
}

@article{viralescape,
	title = {Learning the language of viral evolution and escape},
	journal = {Science},
	year = {2021},
	author = {Hie, B and Zhong, ED and Berger, B and Bryson, B.}
}

@article{mvml,
	author = {Huusari, Riikka and Kadri, Hachem and Capponi, Cécile},
	year = {2018},
	month = {03},
	pages = {},
	title = {Multi-view Metric Learning in Vector-valued Kernel Spaces}
}

@article{mucombo,
	author = {Koço, Sokol and Capponi, Cécile},
	year = {2013},
	month = {03},
	pages = {},
	title = {On multi-class learning through the minimization of the confusion matrix norm},
	volume = {29},
	journal = {Journal of Machine Learning Research}
}

@inproceedings{mkl,
	author = {Kloft, Marius and Brefeld, Ulf and Laskov, Pavel and M\"{u}ller, Klaus-Robert and Zien, Alexander and Sonnenburg, S\"{o}ren},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {Y. Bengio and D. Schuurmans and J. Lafferty and C. Williams and A. Culotta},
	pages = {},
	publisher = {Curran Associates, Inc.},
	title = {Efficient and Accurate Lp-Norm Multiple Kernel Learning},
	url = {https://proceedings.neurips.cc/paper/2009/file/d516b13671a4179d9b7b458a6ebdeb92-Paper.pdf},
	volume = {22},
	year = {2009}
}


@article{mv-late,
	author = {Gupta, Akshansh and Khan, Riyaj and Singh, Vinod and Tanveer, M. and Kumar, Dhirendra and Chakraborti, Anirban and Pachori, Ram},
	year = {2020},
	month = {07},
	pages = {},
	title = {A novel approach for classification of mental tasks using multiview ensemble learning (MEL)},
	volume = {417},
	journal = {Neurocomputing},
	doi = {10.1016/j.neucom.2020.07.050}
}

@article{mv-bio,
	doi = {10.1371/journal.pcbi.1007677},
	author = {Nguyen, Nam D. and Wang, Daifeng},
	journal = {PLOS Computational Biology},
	publisher = {Public Library of Science},
	title = {Multiview learning for understanding functional multiomics},
	year = {2020},
	month = {04},
	volume = {16},
	url = {https://doi.org/10.1371/journal.pcbi.1007677},
	pages = {1-26},
	abstract = {The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning—an emerging machine learning field—and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data’s heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data—specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.},
	number = {4}
}

@InProceedings{mv-qaram, 
	title = {The Multi-Task Learning View of Multimodal Data}, author = {Hachem Kadri and Stephane Ayache and Cécile Capponi and Sokol Koço and François-Xavier Dupé and Emilie Morvant}, booktitle = {Proceedings of the 5th Asian Conference on Machine Learning}, pages = {261--276}, year = {2013}, editor = {Cheng Soon Ong and Tu Bao Ho}, volume = {29}, series = {Proceedings of Machine Learning Research}, address = {Australian National University, Canberra, Australia}, month = {13--15 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v29/Kadri13.pdf}, url = {http://proceedings.mlr.press/v29/Kadri13.html}, abstract = {We study the problem of learning from multiple views using kernel methods in a supervised setting. We approach this problem from a multi-task learning point of view and illustrate how to capture the interesting multimodal structure of the data using multi-task kernels. Our analysis shows that the multi-task perspective offers the flexibility to design more efficient multiple-source learning algorithms, and hence the ability to exploit multiple descriptions of the data. In particular, we formulate the multimodal learning framework using vector-valued reproducing kernel Hilbert spaces, and we derive specific multi-task kernels that can operate over multiple modalities. Finally, we analyze the vector-valued regularized least squares algorithm in this context, and demonstrate its potential in a series of experiments with a real-world multimodal data set.} } 

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

@article{MADELON,
	Author={Guyon, Isabelle },
	Journal={},
	Title={Design of experiments for the NIPS 2003 variable selection benchmark},
	Year={2003}
}



@Article{noisiy_ada,
	author={Long, Philip M.
	and Servedio, Rocco A.},
	title={Random classification noise defeats all convex potential boosters},
	journal={Machine Learning},
	year={2010},
	month={Mar},
	day={01},
	volume={78},
	number={3},
	pages={287-304},
	abstract={A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set. This class includes AdaBoost, LogitBoost, and other widely used and well-studied boosters. In this paper we show that for a broad class of convex potential functions, any such boosting algorithm is highly susceptible to random classification noise. We do this by showing that for any such booster and any nonzero random classification noise rate $\eta$, there is a simple data set of examples which is efficiently learnable by such a booster if there is no noise, but which cannot be learned to accuracy better than 1/2 if there is random classification noise at rate $\eta$. This holds even if the booster regularizes using early stopping or a bound on the L1 norm of the voting weights. This negative result is in contrast with known branching program based boosters which do not fall into the convex potential function framework and which can provably learn to high accuracy in the presence of random classification noise.},
	issn={1573-0565},
	doi={10.1007/s10994-009-5165-z},
	url={https://doi.org/10.1007/s10994-009-5165-z}
}

@article{anil,
	title = {Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters},
	journal = {Neurocomputing},
	volume = {358},
	pages = {81-92},
	year = {2019},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2019.04.072},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231219306630},
	author = {Goyal, Anil and Morvant, Emilie and Germain, Pascal and Amini, Massih-Reza},
	keywords = {Multiview Learning, PAC-Bayes, Boosting},
	abstract = {In this paper we propose a boosting based multiview learning algorithm, referred as PB−MVBoost, which iteratively learns (i) weights over view-specific voters capturing view-specific information, and (ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specific classifiers and the diversity between the views. We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.}
}


@article{2-boost,
	TITLE = {{Boosting Classifiers built from Different Subsets of Features}},
	AUTHOR = {Janodet, Jean-Christophe and Sebban, Marc and Suchier, Henri-Maxime},
	URL = {https://hal.archives-ouvertes.fr/hal-00403242},
	JOURNAL = {{Fundamenta Informaticae}},
	PUBLISHER = {{Polskie Towarzystwo Matematyczne}},
	VOLUME = {94},
	NUMBER = {2009},
	PAGES = {1-21},
	YEAR = {2009},
	MONTH = Jul,
	DOI = {10.3233/FI-2009-131},
	PDF = {https://hal.archives-ouvertes.fr/hal-00403242/file/jss.pdf},
	HAL_ID = {hal-00403242},
	HAL_VERSION = {v1},
}


@book{bouquin_bayes,
	abstract = {This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning---including probability and statistics, artificial intelligence, and neural networks---unifying them all in a logical and coherent manner.},
	added-at = {2017-05-08T14:37:30.000+0200},
	address = {New York},
	author = {Mitchell, Tom M.},
	biburl = {https://www.bibsonomy.org/bibtex/23e79734ee1a6e49aee02ffd108224d1c/flint63},
	file = {eBook:1900-99/Mitchell97.pdf:PDF;McGraw-Hill Product page:http\://www.mhprofessional.com/product.php?isbn=0070428077:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/0070428077/:URL},
	groups = {public},
	interhash = {479a66c32badb3a455fbdcf8e6633a5d},
	intrahash = {3e79734ee1a6e49aee02ffd108224d1c},
	isbn = {978-0-07-042807-2},
	keywords = {01624 105 book shelf ai learn algorithm},
	publisher = {McGraw-Hill},
	timestamp = {2017-07-13T17:10:10.000+0200},
	title = {Machine Learning},
	username = {flint63},
	year = 1997
}


@article{complem_vote,
	author = {Kuncheva, Ludmila and Whitaker, Chris},
	year = {2003},
	month = {05},
	pages = {181-207},
	title = {Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy},
	volume = {51},
	journal = {Machine Learning},
	doi = {10.1023/A:1022859003006}
}

@InProceedings{mumbo,
	author={Ko\c{c}o, Sokol and Capponi, C{\'e}cile},
	editor={Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato
	and Vazirgiannis, Michalis},
	title={A Boosting Approach to Multiview Classification with Cooperation},
	booktitle={Proceedings of the 2011 European Conference on Machine Learning
	and Knowledge Discovery in Databases - Volume Part II},
	year={2011},
	location={Athens, Greece},
	publisher={Springer-Verlag},
	address={Berlin, Heidelberg},
	pages={209--228},
	numpages = {20},
	isbn={978-3-642-23783-6},
	url={https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14},
	keywords={boosting, classification, multiview learning,
	supervised learning},
}

@article{fusion,
	title = {Multisensor data fusion: A review of the state-of-the-art},
	journal = {Information Fusion},
	volume = {14},
	number = {1},
	pages = {28-44},
	year = {2013},
	issn = {1566-2535},
	doi = {https://doi.org/10.1016/j.inffus.2011.08.001},
	url = {https://www.sciencedirect.com/science/article/pii/S1566253511000558},
	author = {Bahador Khaleghi and Alaa Khamis and Fakhreddine O. Karray and Saiedeh N. Razavi},
	keywords = {Multisensor data fusion, Taxonomy, Fusion methodologies},
	abstract = {There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.}
}

@inproceedings{fusions,
	author = {Snoek, Cees and Worring, Marcel and Smeulders, Arnold},
	year = {2005},
	month = {01},
	pages = {399-402},
	title = {Early versus late fusion in semantic video analysis},
	journal = {Proceedings of the 13th ACM International Conference on Multimedia, MM 2005},
	doi = {10.1145/1101149.1101236}
}

@article{multisurvey,
	title={Multimodal machine learning: A survey and taxonomy},
	author={Baltru{\v{s}}aitis, Tadas and Ahuja, Chaitanya and Morency, Louis-Philippe},
	journal={IEEE transactions on pattern analysis and machine intelligence},
	volume={41},
	number={2},
	pages={423--443},
	year={2018},
	publisher={IEEE}
}

@ARTICLE{mv_nmf,
	
	author={Y. {Wang} and L. {Wu} and X. {Lin} and J. {Gao}},
	
	journal={IEEE Transactions on Neural Networks and Learning Systems}, 
	
	title={Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization}, 
	
	year={2018},
	
	volume={29},
	
	number={10},
	
	pages={4833-4843},
	
	doi={10.1109/TNNLS.2017.2777489}}

@article{mv_nmf2,
	title = {Multi-view clustering via multi-manifold regularized non-negative matrix factorization},
	journal = {Neural Networks},
	volume = {88},
	pages = {74-89},
	year = {2017},
	issn = {0893-6080},
	doi = {https://doi.org/10.1016/j.neunet.2017.02.003},
	url = {https://www.sciencedirect.com/science/article/pii/S089360801730028X},
	author = {Linlin Zong and Xianchao Zhang and Long Zhao and Hong Yu and Qianli Zhao},
	keywords = {Non-negative matrix factorization, Multi-view clustering, Multi-manifold, Locally linear embedding (LLE)},
	abstract = {Non-negative matrix factorization based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, non-negative matrix factorization fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized non-negative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF incorporates consensus manifold and consensus coefficient matrix with multi-manifold regularization to preserve the locally geometrical structure of the multi-view data space. We use two methods to construct the consensus manifold and two methods to find the consensus coefficient matrix, which leads to four instances of the framework. Experimental results show that the proposed algorithms outperform existing non-negative matrix factorization based algorithms for multi-view clustering.}
}


@inproceedings{cotraining,
	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}
}







@phdthesis{jfr,
	author       = {Roy, Jean-Francis}, 
	title        = {Apprentissage automatique avec garanties de généralisation à l'aide de méthodes d'ensemble maximisant le désaccord},
	school       = {Université Laval},
	year         = 2018,
	month        = 2,
}


@Article{papier2,
	author={Machin, Mac and Bidule, Bid and Gautheron, Léo},
	title = 	 {Un autre titre de papier},
	journal = 	 {Revue d'apprentissage automatique},
	year = 	 {2014},
	OPTkey = 	 {},
	OPTvolume = 	 {},
	OPTnumber = 	 {},
	OPTpages = 	 {},
	OPTmonth = 	 {},
	OPTnote = 	 {},
	OPTannote = 	 {}
}

@INPROCEEDINGS{resnet,  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},   title={Deep Residual Learning for Image Recognition},   year={2016},  volume={},  number={},  pages={770-778},  doi={10.1109/CVPR.2016.90}}

@book{dt,
	added-at = {2020-05-07T22:53:11.000+0200},
	address = {Monterey, CA},
	author = {Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, C. J.},
	biburl = {https://www.bibsonomy.org/bibtex/27f293aa2bdfd10960ef36928f2795f1d/flashspys},
	interhash = {61f3e6d61ba17bb493014bd1c6dfa670},
	intrahash = {7f293aa2bdfd10960ef36928f2795f1d},
	keywords = {ma treelearning},
	publisher = {Wadsworth and Brooks},
	serial = {bre84a},
	timestamp = {2020-05-07T22:53:11.000+0200},
	title = {Classification and Regression Trees},
	year = {1984}
}


@inproceedings{gini,
	title = {Splitting Rules},
	author = {Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, C. J.},
	booktitle = {Classification and Regression Trees},
	pages = {93--129},
	year = {1984},
	publisher = {Wadsworth and Brooks},
}




@inproceedings{lampert2009learning,
	title={Learning to detect unseen object classes by between-class attribute transfer},
	author={Lampert, Christoph H and Nickisch, Hannes and Harmeling, Stefan},
	booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition},
	pages={951--958},
	year={2009},
	organization={IEEE}
}





@article{gradientboosting,
	author = {Jerome H. Friedman},
	title = {{Greedy function approximation: A gradient boosting machine.}},
	volume = {29},
	journal = {The Annals of Statistics},
	number = {5},
	publisher = {Institute of Mathematical Statistics},
	pages = {1189 -- 1232},
	keywords = {boosting, decision trees, Function estimation, robust nonparametric regression},
	year = {2001},
	doi = {10.1214/aos/1013203451},
	URL = {https://doi.org/10.1214/aos/1013203451}
}




@Article{top10,
	author={Wu, Xindong
	and Kumar, Vipin
	and Ross Quinlan, J.
	and Ghosh, Joydeep
	and Yang, Qiang
	and Motoda, Hiroshi
	and McLachlan, Geoffrey J.
	and Ng, Angus
	and Liu, Bing
	and Yu, Philip S.
	and Zhou, Zhi-Hua
	and Steinbach, Michael
	and Hand, David J.
	and Steinberg, Dan},
	title={Top 10 algorithms in data mining},
	journal={Knowledge and Information Systems},
	year={2008},
	month={Jan},
	day={01},
	volume={14},
	number={1},
	pages={1-37},
	abstract={This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.},
	issn={0219-3116},
	doi={10.1007/s10115-007-0114-2},
	url={https://doi.org/10.1007/s10115-007-0114-2}
}



@article{regulada,
	author = {Rätsch, Gunnar and Onoda, Takashi and Müller, Klaus-Robert},
	year = {1999},
	month = {10},
	pages = {},
	journal={NIPS},
	title = {Regularizing AdaBoost}
}


@Inbook{Capponi2019mucombo,
	author="Capponi, C{\'e}cile
	and Ko{\c{c}}o, Sokol",
	editor="P, Deepak
	and Jurek-Loughrey, Anna",
	title="Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods",
	bookTitle="Linking and Mining Heterogeneous and Multi-view Data",
	year="2019",
	publisher="Springer International Publishing",
	address="Cham",
	pages="161--182",
	abstract="In this paper, we address the problem of learning from imbalanced multi-class datasets in a supervised setting when multiple descriptions of the data---also called views---are available. Each view incorporates various information on the examples, and in particular, depending on the task at hand, each view might be better at recognizing only a subset of the classes. Establishing a sort of cooperation between the views is needed for all the classes to be equally recognized---a crucial problem particularly for imbalanced datasets. The novelty of our work consists in capitalizing on the complementariness of the views so that each class can be processed by the most appropriate view(s), thus improving the per-class performances of the final classifier. The main contribution of this paper are two ensemble learning methods based on recent theoretical works on the use of the confusion matrix's norm as an error measure, while empirical results show the benefits of the proposed approaches.",
	isbn="978-3-030-01872-6",
	doi="10.1007/978-3-030-01872-6_7",
	url="https://doi.org/10.1007/978-3-030-01872-6_7"
}

@Inbook{linesearch,
	author="Jorge Nocedal and Stephen Wright",
	editor="Jorge Nocedal and Stephen Wright",
	title="Line Search Methods",
	bookTitle="Numerical Optimization",
	year="1999",
	publisher="Springer International Publishing",
	address="NY",
	pages="34–63",
	isbn="978-0-387-22742-9",
	doi="https://doi.org/10.1007/b98874",
	url="https://link.springer.com/book/10.1007/b98874"
}





@Article{diversity,
	author={Kuncheva, Ludmila I.
	and Whitaker, Christopher J.},
	title={Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy},
	journal={Machine Learning},
	year={2003},
	month={May},
	day={01},
	volume={51},
	number={2},
	pages={181-207},
	abstract={Diversity among the members of a team of classifiers is deemed to be a key issue in classifier combination. However, measuring diversity is not straightforward because there is no generally accepted formal definition. We have found and studied ten statistics which can measure diversity among binary classifier outputs (correct or incorrect vote for the class label): four averaged pairwise measures (the Q statistic, the correlation, the disagreement and the double fault) and six non-pairwise measures (the entropy of the votes, the difficulty index, the Kohavi-Wolpert variance, the interrater agreement, the generalized diversity, and the coincident failure diversity). Four experiments have been designed to examine the relationship between the accuracy of the team and the measures of diversity, and among the measures themselves. Although there are proven connections between diversity and accuracy in some special cases, our results raise some doubts about the usefulness of diversity measures in building classifier ensembles in real-life pattern recognition problems.},
	issn={1573-0565},
	doi={10.1023/A:1022859003006},
	url={https://doi.org/10.1023/A:1022859003006}
}

@article{krogh1995validation,
	title={Validation, and Active Learning},
	author={Krogh, Anders and Vedelsby, Jesper},
	journal={Advances in neural information processing systems 7},
	volume={7},
	pages={231},
	year={1995},
	publisher={MIT press}
}

@article{hansen1990neural,
	title={Neural network ensembles},
	author={Hansen, Lars Kai and Salamon, Peter},
	journal={IEEE transactions on pattern analysis and machine intelligence},
	volume={12},
	number={10},
	pages={993--1001},
	year={1990},
	publisher={IEEE}
}

@INPROCEEDINGS{disagorigin,
	author = {David B. Skalak},
	title = {The Sources of Increased Accuracy for Two Proposed Boosting Algorithms},
	booktitle = {In Proc. American Association for Arti Intelligence, AAAI-96, Integrating Multiple Learned Models Workshop},
	year = {1996},
	pages = {120--125}
}

@inproceedings{cbound,
	author = {Lacasse, Alexandre and Laviolette, Fran\c{c}ois and Marchand, Mario and Germain, Pascal and Usunier, Nicolas},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
	pages = {},
	publisher = {MIT Press},
	title = {PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier},
	url = {https://proceedings.neurips.cc/paper/2006/file/779efbd24d5a7e37ce8dc93e7c04d572-Paper.pdf},
	volume = {19},
	year = {2007}
}

@InProceedings{divqual,
	author="Cunningham, P{\'a}draig
	and Carney, John",
	editor="L{\'o}pez de M{\'a}ntaras, Ramon
	and Plaza, Enric",
	title="Diversity versus Quality in Classification Ensembles Based on Feature Selection",
	booktitle="Machine Learning: ECML 2000",
	year="2000",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="109--116",
	abstract="Feature subset-selection has emerged as a useful technique for creating diversity in ensembles --- particularly in classification ensembles. In this paper we argue that this diversity needs to be monitored in the creation of the ensemble. We propose an entropy measure of the outputs of the ensemble members as a useful measure of the ensemble diversity. Further, we show that using the associated conditional entropy as a loss function (error measure) works well and the entropy in the ensemble predicts well the reduction in error due to the ensemble. These measures are evaluated on a medical prediction problem and are shown to predict the performance of the ensemble well. We also show that the entropy measure of diversity has the added advantage that it seems to model the change in diversity with the size of the ensemble.",
	isbn="978-3-540-45164-8"
}

@article{knn,
	ISSN = {03067734, 17515823},
	URL = {http://www.jstor.org/stable/1403797},
	author = {Evelyn Fix and J. L. Hodges},
	journal = {International Statistical Review / Revue Internationale de Statistique},
	number = {3},
	pages = {238--247},
	publisher = {[Wiley, International Statistical Institute (ISI)]},
	title = {Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties},
	urldate = {2023-02-13},
	volume = {57},
	year = {1989}
}

@article{germain15a,
	author  = {Pascal Germain and Alexandre Lacasse and Francois Laviolette and Mario March and Jean-Francis Roy},
	title   = {Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm},
	journal = {Journal of Machine Learning Research},
	year    = {2015},
	volume  = {16},
	number  = {26},
	pages   = {787--860},
	url     = {http://jmlr.org/papers/v16/germain15a.html}
}

@article{metagenomes,
	title={Cross-Study Analyses of Gut Microbiomes from Healthy and Obese Individuals},
	author={Deraspe, Maxime and Burdet, Charles and Dominguez, Juan Manuel and Laviolette, Fran{\c{c}}ois and Roy, Paul H and Corbeil, Jacques},
	year={2020}
}
@article{svm,
	title={Support-vector networks},
	author={Cortes, Corinna and Vapnik, Vladimir},
	journal={Machine learning},
	volume={20},
	number={3},
	pages={273--297},
	year={1995},
	publisher={Springer}
} 

@article{medna,
	title = {Prédiction de l’observance à court terme d’une diète méditerranéenne grâce à une approche métabolomique non ciblée et des études d’alimentation contrôlées},
	journal = {Nutrition Clinique et Métabolisme},
	year = {2020},
	url = {https://www.sciencedirect.com/science/article/pii/S0985056220304386},
	author = {D. Brassard and F. Brière and N. Boucher and P.-L. Plante and J. Corbeil and S. Lemieux and  B. Lamarche}
}

@article{l2boost,
	author = {Peter Bühlmann and Bin Yu},
	title = {Boosting With the L2 Loss},
	journal = {Journal of the American Statistical Association},
	volume = {98},
	number = {462},
	pages = {324-339},
	year  = {2003},
	publisher = {Taylor & Francis},
	doi = {10.1198/016214503000125},
	URL = { https://doi.org/10.1198/016214503000125	},
	eprint = { https://doi.org/10.1198/016214503000125}
}









@article{pbsc2007,
  author    = {Fran{\c{c}}ois Laviolette and
               Mario Marchand},
  title     = {PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of
               Sample-Compressed Classifiers},
  journal   = {J. Mach. Learn. Res.},
  volume    = {8},
  pages     = {1461--1487},
  year      = {2007},
  url       = {https://dl.acm.org/doi/10.5555/1314498.1314548},
  doi       = {10.5555/1314498.1314548},
  timestamp = {Thu, 02 Jun 2022 13:58:57 +0200},
  biburl    = {https://dblp.org/rec/journals/jmlr/LavioletteM07.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}