Abstract

In the area of explainable artificial intelligence, Symbolic Regression (SR) has emerged as a promising approach by discovering interpretable mathematical expressions that fit data. However, SR faces two main challenges: most methods are evaluated on scientific datasets with well-understood relationships, limiting generalization, and SR primarily targets single-output regression, whereas many real-world problems involve multi-target outputs with interdependent variables. To address these issues, we propose multi-task regression GINN-LP (MTRGINN-LP), an interpretable neural network for multi-target symbolic regression. By integrating GINN-LP with a multi-task deep learning, the model combines a shared backbone including multiple power-term approximator blocks with task-specific output layers, capturing inter-target dependencies while preserving interpretability. We validate multi-task GINN-LP on practical multi-target applications, including energy efficiency prediction and sustainable agriculture. Experimental results demonstrate competitive predictive performance alongside high interpretability, effectively extending symbolic regression to broader real-world multi-output tasks.

Speaker Bio

Dr. Xishuang Dong is a member of CRI Center for Computational Systems Biology and CREDIT, and Associate Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). He received Ph.D. in computer engineering at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) large language models; (3) interpretable AI; (4) natural language processing.