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

Kaiyue Liu, Lihua Liu, Kaiming Xiao, Xuan Li, Hang Zhang, Yun Zhou and Hongbin Huang ()
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Kaiyue Liu: Laboratory for Big Data and Decision, 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

Mathematics, 2024, vol. 12, issue 17, 1-22

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.

Keywords: continuous optimization; Gaussian cluster; curriculum learning; casual structure (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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