Research map / Label-efficient and robust learning / OOD and anomaly detection
Distribution Shift Is Key to Learning Invariant Prediction
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization (NeurIPS 2024 · similarity 0.89)
- Sufficient Invariant Learning for Distribution Shift (CVPR 2025 · similarity 0.87)
- Generalization vs Specialization under Concept Shift (NeurIPS 2025 · similarity 0.86)
- Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees (ICLR 2025 · similarity 0.86)
- On the Asymptotic Distribution of the Minimum Empirical Risk (ICML 2024 · similarity 0.86)
- Tolerant Algorithms for Learning with Arbitrary Covariate Shift (NeurIPS 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
