Multi-task GINN-LP for Multi-target Symbolic Regression

2025-11-17

AI Summary

Symbolic regression (SR) faces two key limitations in interpretable AI: (1) evaluation relies heavily on idealized scientific datasets, resulting in poor generalization; and (2) dominant approaches support only single-output modeling, hindering the capture of interdependencies among multiple objectives. This work pioneers multi-objective symbolic regression by proposing MTRGINN-LP—a novel architecture that employs a Graph Isomorphism Neural Network (GINN)-based power-term approximation module as a shared backbone, integrated with multi-task learning and an interpretable neural network design. Task-specific output heads and a divide-and-conquer loss function jointly optimize symbolic expression discovery and multi-output prediction. Evaluated on real-world multi-task benchmarks—including energy efficiency forecasting and sustainable agriculture—MTRGINN-LP achieves both high accuracy (12.7% lower MAE) and strong interpretability, while markedly improving cross-domain generalization.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Addressing limited generalization of symbolic regression methods on scientific datasets
• Solving multi-target regression with interdependent variables in real-world problems
• Extending symbolic regression to multi-output tasks while preserving interpretability

Innovation

• Multi-task deep learning for multi-target symbolic regression
• Shared backbone with task-specific output layers
• Power-term approximator blocks preserving model interpretability