Research map / Label-efficient and robust learning / Recognition and long-tail
Calibrated Preference Learning: The Case of Label Ranking
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
- Pairwise Calibrated Rewards for Pluralistic Alignment (NeurIPS 2025 · similarity 0.86)
- Active preference learning for ordering items in- and out-of-sample (NeurIPS 2024 · similarity 0.86)
- ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning (NeurIPS 2025 · similarity 0.86)
- Distributionally Robust Listwise Preference Optimization (NeurIPS 2026 · similarity 0.86)
- Data with High and Consistent Preference Difference Are Better for Reward Model (AAAI 2025 · similarity 0.85)
- Preference Learning Algorithms Do Not Learn Preference Rankings (NeurIPS 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.