A Survey on Multi-Task Learning (1707.08114v3)

Abstract: Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.

A Survey on Multi-Task Learning

The paper by Yu Zhang and Qiang Yang, titled "A Survey on Multi-Task Learning," provides a comprehensive review of Multi-Task Learning (MTL) from the perspectives of algorithmic modeling, applications, and theoretical analysis. MTL is a machine learning paradigm aimed at leveraging useful information contained in multiple related tasks to improve the generalization performance of all tasks. The paper systematically categorizes MTL algorithms, explores their characteristics, and discusses their integration with other learning paradigms.

Algorithmic Modeling

The paper classifies MTL algorithms into five main categories:

1. Feature Learning Approach: This approach involves learning common feature representations for multiple tasks. It includes feature transformation methods and feature selection methods. The feature transformation approach involves methods like multi-layer feedforward neural networks and multi-task sparse coding. The feature selection approach enforces sparsity to identify important features across tasks.
2. Low-Rank Approach: This approach assumes that the parameter matrix of multiple tasks has a low rank, implying that tasks are related through a shared low-dimensional subspace. Methods like the multi-task feature learning algorithm and the trace norm regularization fall under this category.
3. Task Clustering Approach: This approach clusters tasks into groups of similar tasks. Various clustering models, including Bayesian and Dirichlet process models, are employed to discover the underlying task cluster structures.
4. Task Relation Learning Approach: This approach learns quantitative relations (similarities, correlations, covariances) among tasks from the data. Multi-task Gaussian processes and the Multi-Task Relationship Learning (MTRL) method are notable examples.
5. Decomposition Approach: This approach decomposes the parameter matrix into multiple components, each with different regularizations, to capture complex structures among tasks. Examples include models with matrix factorization and hierarchical structures.

Applications

MTL has demonstrated its efficacy across various application domains, including computer vision, bioinformatics, health informatics, speech, NLP, web applications, robotics, and more. Deep MTL models, especially in computer vision and NLP, have shown promising results by sharing hidden layers among tasks.

Theoretical Analysis

Theoretical analysis in MTL focuses on deriving generalization bounds, studying task clustering properties, and feature selection consistency. Table \ref{table-analyses} in the paper systematically compares the generalization bounds derived in different works, revealing a convergence rate of $O\left(\frac{1}{\sqrt{mn_0}}\right)$ in most cases, though recent advancements using local Rademacher complexity have achieved tighter bounds.

Practical Considerations

For the efficient handling of large datasets or tasks, the paper discusses online, parallel, and distributed MTL methods. Techniques like feature hashing and dimensionality reduction are also recommended to manage high-dimensional data effectively.

Future Directions

Several key areas for future research are identified:

• Handling Outlier Tasks: Developing robust MTL methods to mitigate the negative effects of unrelated or noisy tasks remains crucial.
• Enhanced Deep MTL Models: Incorporating flexibility and robustness in deep MTL models will further improve their applicability across varied domains.
• Broadening Applications: Expanding MTL applications to other AI areas such as logic, planning, and diverse learning paradigms like reinforcement learning, semi-supervised learning, and unsupervised learning.

Conclusion

The paper by Yu Zhang and Qiang Yang provides a structured and detailed survey of MTL, offering valuable insights into the development and application of MTL algorithms. By bridging different aspects of MTL, the paper sets a foundation for future work aimed at leveraging the paradigm's full potential in various complex real-world problems.