Research map / Learning theory and algorithms / Sample complexity
Concentric mixtures of Mallows models for top-$k$ rankings: sampling and identifiability
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed inversion-vector sampler instead of the previous top-k sampler | Mallows top-k sampling · Top-k Mallows rankings | sampling time complexity: O(k^2 4^k + k^2 log n) -> O(k log k) | Helped |
| Increase the dispersion-distance ratio c between non-experts and experts | 2-means voter separation · Voter separability | wrongly separated rankings: for c ≥ 9 the percentage of wrongly separated rankings is almost always below 10% | Helped |
| Use expert Borda: identify experts, aggregate their rankings, and fill remaining positions with the full sample | Borda consensus estimation · Consensus estimate with expert and non-expert rankings | distance to consensus | Helped |
| Fit a concentric Mallows mixture rather than a single Mallows model | Mallows model fitting · Real data example with simulated raters | log-likelihood | Helped |
Closest papers by meaning
- Mallows ranking models: maximum likelihood estimate and regeneration (ICML 2019 · similarity 0.88)
- Beyond Kemeny Medians: Consensus Ranking Distributions. Definition, Properties and Statistical Learning (NeurIPS 2026 · similarity 0.87)
- Optimal Sample Complexity of M-wise Data for Top-K Ranking (NeurIPS 2017 · similarity 0.87)
- Identity testing for Mallows model (NeurIPS 2021 · similarity 0.87)
- The Surprising Effectiveness of SP Voting with Partial Preferences (NeurIPS 2024 · similarity 0.85)
- Generalized Top-k Mallows Model for Ranked Choices (NeurIPS 2025 · similarity 0.85)
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