Research map / Label-efficient and robust learning / Semi-supervised learning
Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts
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
- Sequential Harmful Shift Detection Without Labels (NeurIPS 2024 · similarity 0.88)
- Prediction-Powered Active Testing (NeurIPS 2026 · similarity 0.85)
- Anytime-valid, Bayes-assisted, Prediction-Powered Inference (NeurIPS 2025 · similarity 0.84)
- Monitoring Risks in Test-Time Adaptation (NeurIPS 2025 · similarity 0.84)
- Prediction-Powered Semi-Supervised Learning with Online Power Tuning (NeurIPS 2025 · similarity 0.83)
- Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias (NeurIPS 2026 · 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.
