Summary of When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights, by You-wei Luo et al.
When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights
by You-Wei Luo, Chuan-Xian Ren
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new paper explores the limitations of current dataset shift theory and algorithms, focusing on generalized label shift (GLS). The authors derive two informative generalization bounds from a theoretical perspective, proving that the GLS learner is close to the optimal target model. They also show that invariant representation learning is insufficient for generalization and demonstrate the necessity of GLS correction. To address dataset shift, the paper proposes a kernel embedding-based correction algorithm (KECA) that minimizes generalization error and achieves successful knowledge transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study examines how to make machine learning models work well in different situations by understanding when data changes. The researchers look at a special type of change called “dataset shift” where the training data is not the same as the test data. They find that current methods are limited and propose a new approach called Generalized Label Shift (GLS) to help models generalize better. The authors also create an algorithm to make this happen, which they call KECA. This study can help us build more reliable AI systems. |
Keywords
» Artificial intelligence » Embedding » Generalization » Machine learning » Representation learning
