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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
TL;DR
A new method for unlabeled-unlabeled (UU) learning addresses distribution shifts by applying importance weighting to training data. This approach enhances binary classification performance across various learning scenarios without requiring assumptions about the type of distribution shift.
✦ Why It Matters
Implement this importance weighting method to improve your UU learning models under distribution shifts today.
Key Takeaways
- Apply importance weighting to adjust for distribution shifts in UU learning.
- Utilize both training and test UU data for better model performance.
- Explore the method's applicability across PU and noisy label learning.
- Avoid assumptions about distribution shift types to enhance flexibility.
How It Works
The proposed method leverages importance weighting to adapt UU learning to distribution shifts. By estimating importance weights for training data, it effectively aligns the training distribution with the test distribution, minimizing test risk.
This allows the model to learn from both sets of unlabeled data without requiring specific assumptions about the nature of the distribution shifts.