Research map / Probabilistic and scientific modeling / Variational and generative sampling
Learning Plackett-Luce Mixtures from Partial Preferences
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Gibbs sampler instead of GRIM-MCMC in the E-step (ablation) | EM for Plackett-Luce from partial orders · Synthetic, single PL, 10 alternatives | MSE and runtime | Helped |
| use Gibbs sampler instead of GRIM-MCMC in the E-step (ablation) | EM for 3-PL mixtures · Synthetic, 3-PL, 6 alternatives | runtime | Helped |
| use Gibbs sampler instead of GRIM-MCMC in the E-step (ablation) | EM for 3-PL mixtures · Synthetic, 3-PL, 6 alternatives | MSE | No clear effect |
| increase fraction of observed pairwise comparisons p from 0.2 to 1 | ELSR-Gibbs k-PL · Sushi test log-likelihood | test log-likelihood | Helped |
| increase number of mixture components k from 1 to 5 | ELSR-Gibbs k-PL · Sushi test log-likelihood | test log-likelihood | Helped |
Closest papers by meaning
- Learning Mixtures of Plackett-Luce Models from Structured Partial Orders (NeurIPS 2019 · similarity 0.91)
- Efficient and Accurate Learning of Mixtures of Plackett-Luce Models (AAAI 2023 · similarity 0.88)
- Learning Parametric Distributions from Samples and Preferences (ICML 2025 · similarity 0.84)
- Generalizing while preserving monotonicity in comparison-based preference learning models (NeurIPS 2025 · similarity 0.82)
- Active preference learning for ordering items in- and out-of-sample (NeurIPS 2024 · similarity 0.82)
- Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics (ICLR 2025 · similarity 0.82)
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