Summary of Transformation-invariant Learning and Theoretical Guarantees For Ood Generalization, by Omar Montasser et al.
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
by Omar Montasser, Han Shao, Emmanuel Abbe
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper investigates statistical learning under distribution shifts, where train and test distributions can be related by classes of data transformation maps. The authors initiate a theoretical study, exploring learning scenarios where the target class of transformations is either known or unknown. They establish learning rules and algorithmic reductions to Empirical Risk Minimization (ERM), accompanied with learning guarantees. The paper provides upper bounds on the sample complexity in terms of the VC dimension of the class composing predictors with transformations. The authors show that this approach offers a game-theoretic viewpoint on distribution shift, enabling learners to search for optimal predictors and adversaries to search for transformation maps that minimize or maximize worst-case loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how machines learn when the rules change from what they learned before. It’s like trying to solve a puzzle where the pieces are always shifting. The researchers looked at two cases: when we know what kind of changes can happen and when we don’t. They found ways to make predictions that work well even with these changes, and showed that this approach is helpful for both machines learning and people trying to understand how they learn. |
