Research map / Label-efficient and robust learning / Domain adaptation and generalization
Explaining Concept Shift with Interpretable Feature Attribution
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
- TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments (ICML 2025 · similarity 0.88)
- Feature-aware Modulation for Learning from Temporal Tabular Data (NeurIPS 2025 · similarity 0.86)
- Heterogeneous Label Shift: Theory and Algorithm (ICML 2025 · similarity 0.85)
- Generalization vs Specialization under Concept Shift (NeurIPS 2025 · similarity 0.85)
- "What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts (NeurIPS 2026 · similarity 0.84)
- A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data (NeurIPS 2024 · similarity 0.83)
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.