When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Classical machine learning assumes identical data distributions, limiting real-world applicability.
• Dataset shift and invariant representation learning are studied to address changing environments.
• Generalized Label Shift (GLS) offers a promising approach for complex distribution shifts.

Purpose of the Study:

• To explore limitations in current dataset shift theory and algorithms.
• To provide a comprehensive understanding of Generalized Label Shift (GLS).
• To develop novel theoretical insights and a superior correction algorithm for dataset shift.

Main Methods:

• Derived two generalization bounds for GLS learners.
• Proved the sufficiency and necessity of GLS correction for generalization.
• Proposed a kernel embedding-based correction algorithm (KECA) for knowledge transfer.

Main Results:

• Demonstrated the insufficiency of invariant representation learning for complex shifts.
• Showcased the theoretical sufficiency and necessity of GLS correction.
• KECA minimized generalization error and achieved successful knowledge transfer.

Conclusions:

• GLS correction is essential for building generalizable models under dataset shift.
• The proposed KECA algorithm offers a superior method for addressing dataset shift.
• This work provides theoretical and methodological advancements for robust machine learning.