Research map / Learning theory and algorithms / Sample complexity
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use item features and feature correlations for ranking | IPR vs. LRPR · Synthetic simulations | sample complexity | Helped |
| use item features for ranking | IPR vs. LRPR · Sushi | recovery of top-ranked dishes | Helped |
| use item features for ranking | IPR · Car | qualitative preference ranking | No clear effect |
Closest papers by meaning
- Preference Modeling with Context-Dependent Salient Features (ICML 2020 · similarity 0.89)
- Active preference learning for ordering items in- and out-of-sample (NeurIPS 2024 · similarity 0.86)
- From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking (AAAI 2025 · similarity 0.86)
- RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank (ICLR 2025 · similarity 0.85)
- Preference-Based Dynamic Ranking Structure Recognition (NeurIPS 2025 · similarity 0.85)
- Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization (ICML 2025 · 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.
