Research map / Learning theory and algorithms / Sample complexity
Efficient and Accurate Learning of Mixtures of Plackett-Luce Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use EM-LSR with weighted Luce spectral ranking for the M-step instead of EM-CML | PL mixture learning · ML-10M-derived rankings, ML (100) and ML (200) | test log-likelihood: -330 -> -326; -799 -> -787 | Helped |
| Use EM-LSR instead of EM-CML | PL mixture learning · Sushi test | test log-likelihood: -14.0 -> -13.6 | Helped |
| Use EM-LSR instead of EM-CML | PL mixture learning · APA test | test log-likelihood: -4.6 -> -4.6 | No clear effect |
| Use spectral initialization instead of random or GMM initialization | EM-based PL mixture learning · Synthetic datasets | estimation error | Helped |
| Use weighted LSR for the M-step rather than the stochastic clustering and unweighted LSR M-step | EM-based PL mixture learning · Supplementary experiments | estimation error | Helped |
Closest papers by meaning
- Learning Mixtures of Plackett-Luce Models (ICML 2016 · similarity 0.90)
- Learning Plackett-Luce Mixtures from Partial Preferences (AAAI 2019 · similarity 0.88)
- Learning Mixtures of Plackett-Luce Models from Structured Partial Orders (NeurIPS 2019 · similarity 0.87)
- Big Learning Expectation Maximization (AAAI 2024 · similarity 0.86)
- Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models (NeurIPS 2024 · similarity 0.84)
- Learning Mixtures of Experts with EM: A Mirror Descent Perspective (ICML 2025 · similarity 0.83)
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