MALSAR: Multi-task learning via Structural Regularization

The MALSAR (Multi-tAsk Learning via StructurAl Regularization) package includes the following multi-task learning algorithms:

• Mean-Regularized Multi-Task Learning
• Multi-Task Learning with Joint Feature Selection
• Robust Multi-Task Feature Learning
• Trace-Norm Regularized Multi-Task Learning
• Alternating Structural Optimization
• Incoherent Low-Rank and Sparse Learning
• Robust Low-Rank Multi-Task Learning
• Clustered Multi-Task Learning
• Multi-Task Learning with Graph Structures
• Disease Progression Models
• Incomplete Multi-Source Fusion (iMSF)
• Multi-Stage Multi-Source Fusion
• Multi-Task Clustering
• Multi-task Feature Learning with Calibration
• Patient Densifier (Multi-Task Matrix Completion)

If you have any questions regarding MALSAR, please contact Jiayu Zhou at jiayuz@msu.edu.

Manual

The manual for the lastest version can be found here.

Tutorial

We gave a tutorial on multi-task learning at the Twelfth SIAM Internation conference on Data Mining (SDM'12). The tutorial slides can be found here.

Citation

In citing MALSAR in your papers, you can use the following:
Zhou, Jiayu, Jianhui Chen, and Jieping Ye. "MALSAR: Multi-task learning via structural regularization." Arizona State University (2011). https://www.MALSAR.org.

If you use LaTeX, you can use the BibTex entry:

@article{zhou2011malsar,
  title={MALSAR: Multi-task learning via structural regularization},
  author={Zhou, Jiayu and Chen, Jianhui and Ye, Jieping},
  journal={Arizona State University},
  year={2011}
}

Acknowledgement