Tail Task Risk Minimization in Meta-Learning From Theoretical Advances to Practical Strategies.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Optimization Theory

Background:

• Meta learning is crucial for large models, demanding robust performance across diverse tasks.
• Task distributional robustness is essential for real-world applications, with tail risk minimization showing promise.

Purpose of the Study:

• To provide theoretical and practical enhancements for meta learning robustness.
• To investigate tail risk minimization strategies for improved fast adaptation.

Main Methods:

• Reduced the distributionally robust strategy to a max-min optimization problem.
• Utilized Stackelberg equilibrium as the solution concept and estimated convergence rate.
• Incorporated a diversity regularizer in active subset selection for enhanced generalization.

Main Results:

• Derived generalization bounds in the presence of tail risk and connected them with estimated quantiles.
• Systematically analyzed the impact of the diversity regularizer, leading to practical improvements.
• Demonstrated significance, robustness, and scalability across diverse tasks and multimodal large models.

Conclusions:

• The proposed meta-learning strategy significantly improves distributional robustness and generalization.
• The diversity regularizer effectively enhances performance under tail risk minimization.
• The approach is validated across various domains, including few-shot learning and meta reinforcement learning.