Robust Multi-Task Gradient Boosting (R-MTGB)

A robust and scalable multi-task learning (MTL) framework that integrates outlier task detection into a structured gradient boosting process. Built with Python and scikit-learn, R-MTGB is designed to generalize well across heterogeneous task sets and is resilient to task-level noise.

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📘 About

R-MTGB (Robust Multi-Task Gradient Boosting) is a novel ensemble-based learning framework developed to handle task heterogeneity and task-level noise in multi-task learning settings. The model introduces a three-stage boosting architecture:

1. Shared Representation Learning: Learns features common across all tasks.
2. Outlier Task Detection & Weighting: Optimizes regularized, task-specific parameters to dynamically down-weight noisy or outlier tasks.
3. Task-Specific Fine-Tuning: Refines models individually to capture task-specific nuances.

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✨ Features

• Multi-task learning with task-specific and shared components.
• Automatic outlier task detection.
• Gradient boosting-based architecture with interpretability.
• Compatible with various loss functions (regression/classification).
• Performance analysis with per-task metrics.
• Synthetic data generator for benchmarking.
• Scikit-learn compatible design.

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💻 Installation

Clone the repository and install dependencies using requirements

git clone https://github.com/GAA-UAM/R-MTGB.git
cd R-MTGB
pip install -r requirements.txt

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🔑 License

The package is licensed under the GNU Lesser General Public License v2.1.

📚 Citations

If you use R-MTGB in your research or work, please consider citing this project using the following citation format.

@article{EMAMI2025130696,
    title         = {Robust-Multi-Task Gradient Boosting},
    journal       = {Expert Systems with Applications},
    pages         = {130696},
    year          = {2025},
    issn          = {0957-4174},
    doi           = {https://doi.org/10.1016/j.eswa.2025.130696},
    url           = {https://www.sciencedirect.com/science/article/pii/S0957417425043118},
    author        = {Seyedsaman Emami and Gonzalo {Mart\'{\i}nez-Mu\~noz} and Daniel Hern\'{a}ndez-Lobato}
}

👨‍💻 Authors

• Seyedsaman (Saman) Emami
• Gonzalo Martínez-Muñoz
• Daniel Hernández-Lobato

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Documentation

To get started with this project, please refer to the Wiki."

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

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💾 Release Information

Version

0.0.1

Updated

05 June 2025

Date-released

26 Jan 2024