Research map / Learning theory and algorithms / Kernels and regression theory
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Estimate the Meta-SGLD generalization bound using gradient incoherence rather than gradient norm | Meta-SGLD · Synthetic 2D mean estimation | generalization-bound magnitude: improved by a factor of 10 to 100 | Helped |
| Estimate the Meta-SGLD generalization bound using gradient incoherence rather than gradient norm | Meta-SGLD · Omniglot, m_tr=8 and 15 | generalization-bound magnitude | Helped |
| Estimate the Meta-SGLD generalization bound using gradient incoherence rather than gradient norm | Meta-SGLD · Omniglot, m_tr=1 | generalization-bound magnitude | No clear effect |
| Use m_tr=1 rather than the other evaluated training-set sizes | Meta-SGLD · Synthetic 2D mean estimation | actual generalization gap | Hurt |
| Use m_va=8 rather than the other evaluated validation-set sizes | Meta-SGLD · Omniglot | generalization-bound magnitude | Helped |
Closest papers by meaning
- Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability (NeurIPS 2021 · similarity 0.93)
- Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective (ICML 2024 · similarity 0.90)
- On the Stability and Generalization of Meta-Learning (NeurIPS 2024 · similarity 0.90)
- Certification-Enhanced Generalization Bounds (NeurIPS 2026 · similarity 0.89)
- Slicing Mutual Information Generalization Bounds for Neural Networks (ICML 2024 · similarity 0.89)
- Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks (ICML 2025 · similarity 0.89)
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