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

• URL: http://arxiv.org/abs/2511.13463v1
• Date: Mon, 17 Nov 2025 15:07:41 GMT
• Title: Multi-task GINN-LP for Multi-target Symbolic Regression
• Authors: Hussein Rajabu, Lijun Qian, Xishuang Dong,
• Abstract summary: We propose multi-task regression GINN-LP (MTRGINN-LP), an interpretable neural network for multi-target symbolic regression.<n>We validate multi-task GINN-LP on practical multi-target applications, including energy efficiency prediction and sustainable agriculture.
• Score: 0.3670422696827525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training [53.93696896939915]
  Training tool-use agents typically rely on Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks.<n>We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology.<n>TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-02T10:38:54Z)
• FDRMFL:Multi-modal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning [4.453671369861554]
  This study focuses on the feature extraction problem in multi-modal data regression.<n>It addresses three core challenges in real-world scenarios: limited and non-IID data, effective extraction and fusion of multi-modal information, and susceptibility to catastrophic forgetting in model learning.
  arXiv  Detail & Related papers  (2025-11-30T17:13:35Z)
• Universal Retrieval for Multimodal Trajectory Modeling [12.160448446091607]
  Trajectory data holds significant potential for enhancing AI agent capabilities.<n>We introduce Multimodal Trajectory Retrieval, bridging the gap between universal retrieval and agent-centric trajectory modeling.
  arXiv  Detail & Related papers  (2025-06-27T09:50:38Z)
• Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning [71.3533541927459]
  We propose a novel data selection paradigm termed Activation Reasoning Potential (RAP)<n>RAP identifies cognitive samples by estimating each sample's potential to stimulate genuine multi-modal reasoning.<n>Our RAP method consistently achieves superior performance using only 9.3% of the training data, while reducing computational costs by over 43%.
  arXiv  Detail & Related papers  (2025-06-05T08:40:24Z)
• LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding [4.759109475818876]
  Implicit Neural Representations (INRs) are proving to be a powerful paradigm in unifying task modeling across diverse data domains.<n>We introduce LIFT, a novel, high-performance framework that captures multiscale information through meta-learning.<n>We also introduce ReLIFT, an enhanced variant of LIFT that incorporates residual connections and expressive frequency encodings.
  arXiv  Detail & Related papers  (2025-03-19T17:00:58Z)
• Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation [61.64052577026623]
  Real-world multi-view datasets are often heterogeneous and imperfect.<n>We propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment.<n>Our RML is self-supervised and can also be applied for downstream tasks as a regularization.
  arXiv  Detail & Related papers  (2025-03-06T07:01:08Z)
• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes [56.714690083118406]
  In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures has been shown to yield significant benefits to the sample efficiency compared to single-task RL. We investigate whether such a benefit can extend to more general sequential decision making problems, such as partially observable MDPs (POMDPs) and more general predictive state representations (PSRs) We propose a provably efficient algorithm UMT-PSR for finding near-optimal policies for all PSRs, and demonstrate that the advantage of multi-task learning manifests if the joint model class of PSR
  arXiv  Detail & Related papers  (2023-10-20T14:50:28Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• Soft Hierarchical Graph Recurrent Networks for Many-Agent Partially Observable Environments [9.067091068256747]
  We propose a novel network structure called hierarchical graph recurrent network(HGRN) for multi-agent cooperation under partial observability. Based on the above technologies, we proposed a value-based MADRL algorithm called Soft-HGRN and its actor-critic variant named SAC-HRGN.
  arXiv  Detail & Related papers  (2021-09-05T09:51:25Z)
• Multi-task Over-the-Air Federated Learning: A Non-Orthogonal Transmission Approach [52.85647632037537]
  We propose a multi-task over-theair federated learning (MOAFL) framework, where multiple learning tasks share edge devices for data collection and learning models under the coordination of a edge server (ES) Both the convergence analysis and numerical results demonstrate that the MOAFL framework can significantly reduce the uplink bandwidth consumption of multiple tasks without causing substantial learning performance degradation.
  arXiv  Detail & Related papers  (2021-06-27T13:09:32Z)
• Multi-target regression via output space quantization [0.3007949058551534]
  The proposed method, called MRQ, is based on the idea of quantizing the output space in order to transform the multiple continuous targets into one or more discrete ones. Experiments on a large collection of benchmark datasets show that MRQ is both highly scalable and also competitive with the state-of-the-art in terms of accuracy.
  arXiv  Detail & Related papers  (2020-03-22T13:57:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.