Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions-Reference-Cited by-同舟云学术

Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

Published:2020-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of The Web Conference 2020
language:
Short-container-title:

Author:

Pan Feiyang¹,Ao Xiang¹,Tang Pingzhong²,Lu Min³,Liu Dapeng³,Xiao Lei³,He Qing⁴

Affiliation:

1. Institute of Computing Technology Chinese Academy of Sciences

2. Tsinghua University

3. Tencent

4. Institute of Computing Technology CAS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3366423.3380154

Reference33 articles.

1. Antonio Bella Cèsar Ferri José Hernández-Orallo and María José Ramírez-Quintana. 2010. Calibration of machine learning models. In Handbook of Research on Machine Learning Applications and Trends: Algorithms Methods and Techniques. IGI Global 128–146. Antonio Bella Cèsar Ferri José Hernández-Orallo and María José Ramírez-Quintana. 2010. Calibration of machine learning models. In Handbook of Research on Machine Learning Applications and Trends: Algorithms Methods and Techniques. IGI Global 128–146.

2. Calibration

3. Yuri Burda Harri Edwards Deepak Pathak Amos Storkey Trevor Darrell and Alexei A Efros. 2018. Large-scale study of curiosity-driven learning. arXiv preprint arXiv:1808.04355(2018). Yuri Burda Harri Edwards Deepak Pathak Amos Storkey Trevor Darrell and Alexei A Efros. 2018. Large-scale study of curiosity-driven learning. arXiv preprint arXiv:1808.04355(2018).

4. The comparison and evaluation of forecasters;DeGroot H;Journal of the Royal Statistical Society: Series D (The Statistician),1983

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Self-boosted Framework for Calibrated Ranking;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. Can I Trust You? Rethinking Calibration with Controllable Confidence Ranking;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. Leveraging Post-Click User Behaviors for Calibrated Conversion Rate Prediction Under Delayed Feedback in Online Advertising;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

5. Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04