Research map / Learning theory and algorithms / Sample complexity
Learning Mixtures of Random Utility Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| initialize E-GMM with GMM output (sandwich algorithm) instead of GMM alone (ablation) | k-RUM learning · synthetic k=2 m=6 and k=4 m=15 | MWRSE | Helped |
| use sandwich instead of GMM alone (ablation) | k-RUM learning · synthetic data | runtime | Hurt |
| initialize E-GMM with GMM (sandwich) instead of random init (ablation) | E-GMM · synthetic data | statistical and computational efficiency | Helped |
| mixture of Gaussian RUMs instead of single Gaussian RUM | RUM · Preflib 209 datasets | AIC (% datasets better): 60.3% | Helped |
| mixture of Gaussian RUMs instead of single Gaussian RUM | RUM · Preflib 209 datasets | BIC (% datasets better): 40.2% | Mixed |
| k-RUM instead of mixtures of Plackett-Luce (k-PL) | mixture models · Preflib 209 datasets | AIC (% datasets better): 60.8% | Helped |
| k-RUM instead of Plackett-Luce | rank models · Preflib 209 datasets | AIC (% datasets better): 90.0% | Helped |
Closest papers by meaning
- Learning Mixtures of Plackett-Luce Models (ICML 2016 · similarity 0.88)
- Composite Marginal Likelihood Methods for Random Utility Models (ICML 2018 · similarity 0.87)
- Tight Bounds for Learning RUMs from Small Slates (NeurIPS 2024 · similarity 0.86)
- Big Learning Expectation Maximization (AAAI 2024 · similarity 0.84)
- 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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