Cited By
View all- Castaño AAlonso JGonzález PPérez Pdel Coz J(2024)QuantificationLib: A Python library for quantification and prevalence estimationSoftwareX10.1016/j.softx.2024.10172826(101728)Online publication date: May-2024
Report on the 3rd International Workshop onLearning to Quantify (LQ 2023)
Pages 25 - 28
Abstract
The 3rd International Workshop on Learning to Quantify (LQ 2023)1 took place on September 18, 2023 in Torino, IT, where it was organised as a satellite event of the 34th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023). Like the main program of the conference, the workshop employed a hybrid format, with all presentations given in presence and with attendees participating in presence or online. This report presents a summary of the workshop, briefly summarising the individual works presented, and touching on the main issues that emerged during the final, open discussion.
References
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M. Bunse. On multi-class extensions of adjusted classify and count. In Proceedings of the 2nd International Workshop on Learning to Quantify (LQ 2022), pages 43--50, Grenoble, IT, 2022.
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M. Bunse. Unification of algorithms for quantification and unfolding. In Proceedings of the Workshop on Machine Learning for Astroparticle Physics and Astronomy, pages 459--468, Hamburg, DE, 2022.
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M. Bunse. Qunfold: Composable quantification and unfolding methods in Python. In Proceedings of the 3rd International Workshop on Learning to Quantify (LQ 2023), pages 1--7, Torino, IT, 2023.
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M. Bunse, P. Gonz´alez, A. Moreo, and F. Sebastiani, editors. Proceedings of the 3rd International Workshop on Learning to Quantify (LQ 2023). Torino, IT, 2023.
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J. Alonso, P. Gonz´alez, and J. J. del Coz. An equivalence analysis of binary quantification methods. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023), pages 6944--6952, Washington, US, 2023.
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Z. Donyavi, A. Serapio, and G. Batista. MC-SQ: A highly accurate ensemble for multi-class quantification. In Proceedings of the 23rd SIAM International Conference on Data Mining (SDM 2023), pages 622--630, Minneapolis, US, 2023.
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B. Dussap, G. Blanchard, and B.-E. Ch´erief-Abdellatif. Label shift quantification with robustness guarantees via distribution feature matching. arXiv:2306.04376 [stat.ML], 2023.
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A. Esuli, A. Fabris, A. Moreo, and F. Sebastiani. Learning to quantify. Springer Nature, Cham, CH, 2023.
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A. Esuli, A. Moreo, F. Sebastiani, and G. Sperduti. A detailed overview of LeQua 2022: Learning to quantify. In Working Notes of the 13th Conference and Labs of the Evaluation Forum (CLEF 2022), Bologna, IT, 2022.
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A. Fabris, A. Esuli, A. Moreo, and F. Sebastiani. Measuring fairness under unawareness of sensitive attributes: A quantification-based approach. Journal of Artificial Intelligence Research, 76:1117--1180, 2023.
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G. Forman. Quantifying trends accurately despite classifier error and class imbalance. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pages 157--166, Philadelphia, US, 2006.
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P. Gonz´alez, N. V. Chawla, and J. J. del Coz. A review on quantification learning. ACM Computing Surveys, 50(5):74:1--74:40, 2017.
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P. Gonz´alez, E. E. Peacock, J. J. Del Coz, and H. M. Sosik. Automatic plankton quantification using deep features. Journal of Plankton Research, 41(4):449--463, 2019.
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K. Kloos, J. D. Karch, Q. A. Meertens, and M. de Rooij. Continuous Sweep: An improved, binary quantifier. arXiv:2308.08387 [stat.ML], 2023.
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P. Latinne, M. Saerens, and C. Decaestecker. Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: Evidence from a multi-class problem in remote sensing. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pages 298-- 305, Williamstown, US, 2001.
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A. Maletzke, D. Moreira dos Reis, E. Cherman, and G. Batista. DyS: A framework for mixture models in quantification. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), pages 4552--4560, Honolulu, US, 2019.
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A. Moreo, A. Esuli, and F. Sebastiani. QuaPy: A Python-based framework for quantification. In Proceedings of the 30th ACM International Conference on Knowledge Management (CIKM 2021), pages 4534-- 4543, Gold Coast, AU, 2021.
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A. Moreo, M. Francisco, and F. Sebastiani. Multi-label quantification. ACM Transactions on Knowledge Discovery and Data, 18(1):Article 4, 2023.
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L. S´anchez, V. Gonz´alez, E. Alegre, and R. Ala´?z. Classification and quantification based on image analysis for sperm samples with uncertain damaged/intact cell proportions. In Proceedings of the 5th International Conference on Image Analysis and Recognition (ICIAR 2008), pages 827--836, P´ovoa de Varzim, PT, 2008.
[21]
D. Tasche. Invariance assumptions for class distribution estimation. In Proceedings of the 3rd International Workshop on Learning to Quantify (LQ 2023), pages 56--71, Torino, IT, 2023.
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Published: 28 March 2024
Published in SIGKDD Volume 25, Issue 2
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View all- Castaño AAlonso JGonzález PPérez Pdel Coz J(2024)QuantificationLib: A Python library for quantification and prevalence estimationSoftwareX10.1016/j.softx.2024.10172826(101728)Online publication date: May-2024
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