Research map / Learning theory and algorithms / Prediction and decision losses
Ranking Distributions based on Noisy Sorting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use insertion sort instead of quick sort as the underlying sorting algorithm in CNS (ablation) | CNS model · 213 PrefLib data sets | KL divergence goodness of fit (win/tie/loss): 197/0/16 (entire), 204/0/9 (half) | Helped |
| generalize CNS to GCNS by relaxing pairwise consistency constraints (insertion sort) (ablation) | CNS model · 213 PrefLib data sets | KL divergence goodness of fit (win/tie/loss): 212/1/0 (entire), 188/0/25 (half) for GCNS_I over CNS_I | Helped |
| generalize CNS to GCNS (quick sort) (ablation) | CNS model · 213 PrefLib data sets | KL divergence goodness of fit (win/tie/loss): 209/0/4 (entire), 195/0/18 (half) for GCNS_Q over CNS_Q | Helped |
| use CNS with insertion sort instead of ISR (Biernacki and Jacques model) | ISR model · 213 PrefLib data sets | KL divergence goodness of fit (win/tie/loss): 197/0/16 (entire), 191/0/22 (half) | Helped |
| use CNS with insertion sort instead of Mallows model | Mallows model · 213 PrefLib data sets | KL divergence goodness of fit (win/tie/loss): 170/0/43 (entire), 167/0/46 (half) | Helped |
| insertion sort instead of quick sort in GCNS (ablation) | GCNS model · 213 PrefLib data sets | KL divergence goodness of fit (win/tie/loss): 212/1/0 (entire), 203/0/10 (half) | Helped |
Closest papers by meaning
- On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons (NeurIPS 2019 · similarity 0.90)
- Optimal Sample Complexity of M-wise Data for Top-K Ranking (NeurIPS 2017 · similarity 0.89)
- On ranking via sorting by estimated expected utility (NeurIPS 2020 · similarity 0.89)
- Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity (ICML 2025 · similarity 0.86)
- Active Ranking and Matchmaking, with Perfect Matchings (ICML 2024 · similarity 0.86)
- Beyond Kemeny Medians: Consensus Ranking Distributions. Definition, Properties and Statistical Learning (NeurIPS 2026 · similarity 0.86)
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