Research map / Learning theory and algorithms / Prediction and decision losses
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ranked top-2 and 2-way moments on linear rankings instead of top-3 moments | 2-PL GMM · Linear orders | MSE | Helped |
| Use choice-2,3,4 moments on linear rankings instead of top-3 moments | 2-PL GMM · Linear orders | MSE | Helped |
| Use ranked top-2 and 2-way moments on linear rankings instead of top-3 moments | 2-PL GMM · Linear orders | runtime | Hurt |
| Use choice-2,3,4 moments on linear rankings instead of top-3 moments | 2-PL GMM · Linear orders | runtime | Hurt |
| Learn from ranked top-2 and 2-way partial orders rather than ranked top-3 partial orders | 2-PL GMM · Partial orders | MSE convergence | Hurt |
| Learn from choice-2,3,4 partial orders rather than ranked top-3 partial orders | 2-PL GMM · Partial orders | MSE convergence | Hurt |
| Use ELSR-Gibbs on choice-2,3,4 partial-order data | ELSR-Gibbs · Choice-2,3,4 partial orders | runtime | Hurt |
| Increase the number of samples | 2-PL estimators other than ELSR-Gibbs · Partial orders | MSE | Helped |
Closest papers by meaning
- Learning Plackett-Luce Mixtures from Partial Preferences (AAAI 2019 · similarity 0.91)
- Learning Mixtures of Plackett-Luce Models (ICML 2016 · similarity 0.89)
- Active preference learning for ordering items in- and out-of-sample (NeurIPS 2024 · similarity 0.84)
- Generalized Top-k Mallows Model for Ranked Choices (NeurIPS 2025 · similarity 0.83)
- Generalizing while preserving monotonicity in comparison-based preference learning models (NeurIPS 2025 · similarity 0.83)
- Selective Preference Aggregation (ICML 2025 · similarity 0.83)
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