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

Author

Listed:
  • Kaiyue Liu

    (Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
    National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Lihua Liu

    (Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China)

  • Kaiming Xiao

    (Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China)

  • Xuan Li

    (Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China)

  • Hang Zhang

    (Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China)

  • Yun Zhou

    (National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Hongbin Huang

    (Laboratory for Big Data and Decision, 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.

Suggested Citation

  • Kaiyue Liu & Lihua Liu & Kaiming Xiao & Xuan Li & Hang Zhang & Yun Zhou & Hongbin Huang, 2024. "CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework," Mathematics, MDPI, vol. 12(17), pages 1-22, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2640-:d:1463741
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    References listed on IDEAS

    as
    1. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, December.
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