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mybib.bib
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@inproceedings{nedelkoski2020self,
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@inproceedings{messaoudi2018search,
title={A search-based approach for accurate identification of log message formats},
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booktitle={2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC)},
year={2018}
}
@inproceedings{he2017drain,
title={Drain: An online log parsing approach with fixed depth tree},
author={He, Pinjia and Zhu, Jieming and Zheng, Zibin and Lyu, Michael R},
booktitle={2017 IEEE international conference on web services (ICWS)},
year={2017},
DOI={10.1109/ICWS.2017.13}
}
@inproceedings{zhu2019tools,
title={Tools and benchmarks for automated log parsing},
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booktitle={2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)},
year={2019},
DOI={10.1109/ICSE-SEIP.2019.00021}
}
@inproceedings{du2016spell,
title={Spell: Streaming parsing of system event logs},
author={Du, Min and Li, Feifei},
booktitle={2016 IEEE 16th International Conference on Data Mining (ICDM)},
year={2016},
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}
@article{devlin2018bert,
title={Bert: Pre-training of deep bidirectional transformers for language understanding},
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journal={arXiv preprint arXiv:1810.04805},
year={2018},
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}
@article{glymour2019review,
title={Review of causal discovery methods based on graphical models},
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journal={Frontiers in genetics},
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}
@incollection{goudet2018learning,
title={Learning functional causal models with generative neural networks},
author={Goudet, Olivier and Kalainathan, Diviyan and Caillou, Philippe and Guyon, Isabelle and Lopez-Paz, David and Sebag, Michele},
booktitle={Explainable and interpretable models in computer vision and machine learning},
year={2018},
publisher={Springer},
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}
@article{shimizu2006linear,
title={A linear non-Gaussian acyclic model for causal discovery},
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journal={Journal of Machine Learning Research},
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@article{zhang2012identifiability,
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@article{seth2007granger,
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@article{hauser2012characterization,
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@article{ramsey2017million,
title={A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images},
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@book{pearl2009causality,
title={Causality},
author={Pearl, Judea},
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@article{nogueira2021causal,
title={Causal discovery in machine learning: Theories and applications},
author={Nogueira, Ana Rita and Gama, Jo{\~a}o and Ferreira, Carlos Abreu},
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@article{le2016fast,
title={A fast PC algorithm for high dimensional causal discovery with multi-core PCs},
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journal={IEEE/ACM transactions on computational biology and bioinformatics},
year={2016},
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@article{buhlmann2010variable,
title={Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm},
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title={Bayesian network learning with the PC algorithm: an improved and correct variation},
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