Research map / Label-efficient and robust learning / Recognition and long-tail
Positive Distribution Shift as a Framework for Understanding Tractable Learning
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
- Tolerant Algorithms for Learning with Arbitrary Covariate Shift (NeurIPS 2024 · similarity 0.88)
- A Fine-Grained Analysis on Distribution Shift (ICLR 2022 · similarity 0.88)
- Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees (ICLR 2025 · similarity 0.87)
- Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization (NeurIPS 2024 · similarity 0.86)
- Efficient Discrepancy Testing for Learning with Distribution Shift (NeurIPS 2024 · similarity 0.85)
- Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability (ICML 2024 · similarity 0.85)
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.
