Report on the 3rd International Workshop onLearning to Quantify (LQ 2023)-Reference-Cited by-同舟云学术

Report on the 3rd International Workshop onLearning to Quantify (LQ 2023)

Published:2024-03-26 Issue:2 Volume:25 Page:25-28
ISSN:1931-0145
Container-title:ACM SIGKDD Explorations Newsletter
language:en
Short-container-title:SIGKDD Explor. Newsl.

Author:

Bunse Mirko¹,Gonzalez Pablo²,Moreo Alejandro³,Sebastiani Fabrizio³

Affiliation:

1. TU Dortmund University, Dortmund, Germany

2. University of Oviedo, 33204 Gijon, Spain

3. Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy

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.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3655103.3655108

Reference21 articles.

1. O. Beijbom, J. Hoffman, E. Yao, T. Darrell, A. Rodriguez-Ramirez, M. Gonzalez-Rivero, and O. Hoegh-Guldberg. Quantification in-the-wild: Datasets and baselines. CoRR abs/1510.04811 (2015). Presented at the NIPS 2015 Workshop on Transfer and Multi-Task Learning, Montreal, CA, 2015.

2. 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.

3. 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.

4. 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.

5. 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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1. QuantificationLib: A Python library for quantification and prevalence estimation;SoftwareX;2024-05