Generalization Bounds of Multitask Learning From Perspective of Vector-Valued Function Learning.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Theoretical Computer Science

Background:

• Multitask learning (MTL) aims to improve generalization by learning multiple related tasks simultaneously.
• Conventional MTL often aggregates task losses, potentially obscuring individual task performance.
• Understanding MTL's generalization and consistency is crucial for effective application.

Purpose of the Study:

• To theoretically analyze the generalization performance of multitask learning (MTL).
• To determine conditions under which MTL surpasses single-task learning (STL).
• To identify criteria ensuring consistent learning across all tasks in an MTL setting.

Main Methods:

• Modeling MTL as learning vector-valued functions (VFs).
• Deriving deviation and symmetrization inequalities for VFs.
• Analyzing task-group relatedness and its impact on generalization bounds.

Main Results:

• MTL outperforms STL when complementary task groups exhibit strong synergistic relatedness.
• A sufficient condition for task consistency in MTL is established: low complexity of individual task function classes.
• A strategy is proposed to evaluate task settings for potential MTL benefits.

Conclusions:

• The vector-valued function approach offers deeper insights into MTL dynamics than traditional methods.
• Synergistic task relatedness is key for MTL's superiority over STL.
• Controlling task complexity is essential for guaranteeing consistent MTL performance.