CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework

Author:

Liu Kaiyue12ORCID,Liu Lihua1ORCID,Xiao Kaiming1ORCID,Li Xuan1,Zhang Hang1,Zhou Yun2ORCID,Huang Hongbin1ORCID

Affiliation:

1. Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China

2. National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China

Abstract

Causal structure learning plays a crucial role in the current field of artificial intelligence, yet existing causal structure learning methods are susceptible to interference from data sample noise and often become trapped in local optima. To address these challenges, this paper introduces a continuous optimization algorithm based on the curriculum learning framework: CL-NOTEARS. The model utilizes the curriculum loss function during training as a priority evaluation metric for curriculum selection and formulates the sample learning sequence of the model through task-level curricula, thereby enhancing the model’s learning performance. A curriculum-based sample prioritization strategy is employed that dynamically adjusts the training sequence based on variations in loss function values across different samples throughout the training process. The results demonstrate a significant reduction in the impact of sample noise in the data, leading to improved model training performance.

Publisher

MDPI AG

Reference48 articles.

1. Causal Structure Learning: A Combinatorial Perspective;Squires;Found. Comput. Math.,2023

2. Zhou, F., He, K., and Ni, Y. (2022, January 1–5). Causal discovery with heterogeneous observational data. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, Eindhoven, The Netherlands.

3. Wang, L., Chignell, M., Jiang, H., Lokuge, S., Mason, G., Fotinos, K., and Katzman, M. (2021, January 27–30). Discovering the Causal Structure of the Hamilton Rating Scale for Depression Using Causal Discovery. Proceedings of the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece.

4. Financial causal sentence recognition based on BERT-CNN text classification;Wan;J. Supercomput.,2022

5. Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques;Algarni;Comput. Math. Methods Med.,2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3