Research map / Learning theory and algorithms / Matrix and tensor methods
Computational and Statistical Tradeoffs in Learning to Rank
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use generalized rank-breaking instead of pairwise rank-breaking | Plackett-Luce ranking estimation · Sushi preferences | estimation error | Helped |
| Use generalized rank-breaking instead of pairwise rank-breaking | Plackett-Luce ranking estimation · Jester | estimation error | Helped |
| Increase the maximum included top-set size M (ablation) | Generalized rank-breaking · Synthetic canonical scenario | estimation error | Helped |
| Include all pairwise relations, including biased relations (ablation) | Pairwise rank-breaking · Synthetic canonical scenario | estimation error | Mixed |
| Use order-1 rank-breaking rather than higher-order rank-breaking (ablation) | Generalized rank-breaking · Synthetic canonical scenario | estimation error | Hurt |
| Increase the top-set size m when the offered set size is fixed (ablation) | Generalized rank-breaking · Synthetic canonical scenario | estimation error | Hurt |
| Increase the number of observed partition edges while using M=3 (ablation) | Generalized rank-breaking · Synthetic canonical scenario | estimation error | Helped |
| Use generalized rank-breaking without revealing within-top-set ordering (ablation) | Plackett-Luce ranking estimation · Sushi preferences, m≤6 | estimation error | No clear effect |
Closest papers by meaning
- Data-driven Rank Breaking for Efficient Rank Aggregation (ICML 2016 · similarity 0.89)
- Learning to Rank by Directly Optimizing Full-Order Probabilities (ICML 2026 · similarity 0.87)
- PiRank: Scalable Learning To Rank via Differentiable Sorting (NeurIPS 2021 · similarity 0.87)
- Active Ranking without Strong Stochastic Transitivity (NeurIPS 2022 · similarity 0.86)
- RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank (ICLR 2025 · similarity 0.84)
- Transductive Conformal Inference for Full Ranking (NeurIPS 2025 · similarity 0.84)
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