Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps.
Keita Sakuma|Ryuta Matsuno|Yoshio Kameda
| Anthology ID: | DBLP:conf/cikm/SakumaMK23 |
|---|---|
| Volume: | Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023 |
| Year: | 2023 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 4259-4263 |
| URL: | https://doi.org/10.1145/3583780.3615238 |
| DOI: | https://doi.org/10.1145/3583780.3615238 |
| DBLP: | conf/cikm/SakumaMK23 |
| BibTeX: |
@inproceedings{sakuma-2023-quantitative,
author = {Keita Sakuma and
Ryuta Matsuno and
Yoshio Kameda},
editor = {Ingo Frommholz and
Frank Hopfgartner and
Mark Lee and
Michael P. Oakes and
Mounia Lalmas-Roelleke and
Min Zhang and
Rodrygo L. T. Santos},
title = {{Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps}},
booktitle = {{Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023}},
pages = {4259--4263},
publisher = {ACM},
year = {2023},
url = {https://doi.org/10.1145/3583780.3615238},
doi = {https://doi.org/10.1145/3583780.3615238}
}