Research map / Learning theory and algorithms / Matrix and tensor methods
Learning to Rank by Directly Optimizing Full-Order Probabilities
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
- PiRank: Scalable Learning To Rank via Differentiable Sorting (NeurIPS 2021 · similarity 0.88)
- Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data (ICML 2026 · similarity 0.88)
- Active Ranking without Strong Stochastic Transitivity (NeurIPS 2022 · similarity 0.87)
- Transductive Conformal Inference for Full Ranking (NeurIPS 2025 · similarity 0.85)
- Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity (ICML 2025 · similarity 0.85)
- Are Greedy Task Orderings Better Than Random in Continual Linear Regression? (NeurIPS 2025 · similarity 0.84)
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.
