Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Conditional independence and graphical models for rankings
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Develops a notion of conditional independence suited to rankings and builds graphical models for ranking distributions on that basis.
Problem
Ranking models over permutations are intractable due to factorial complexity, and standard conditional independence notions do not directly apply to rankings.
Approach
Adapts conditional independence to rankings so that graphical-model-style structural assumptions can be imposed on ranking distributions, reducing complexity and aiding interpretability.
Closest papers by meaning
- Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening (ICML 2017 · similarity 0.89)
- Learning Rich Rankings (NeurIPS 2020 · similarity 0.89)
- On ranking via sorting by estimated expected utility (NeurIPS 2020 · similarity 0.87)
- Learning to Rank from Incomplete Rankings (ICML 2026 · similarity 0.87)
- Foundations of the Theory of Performance-Based Ranking (CVPR 2025 · similarity 0.86)
- Stability and Multigroup Fairness in Ranking with Uncertain Predictions (ICML 2024 · similarity 0.86)
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