Research map / Learning theory and algorithms / Sample complexity
Label Ranking through Nonparametric Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase regression-noise level | Decision trees and random forests · Synthetic LFN and SFN | mean k_tau | Hurt |
| Use shallow trees instead of fully grown decision trees | Decision trees · Synthetic LFN and SFN | mean k_tau | Helped |
| Use fully grown honest random forests instead of fully grown decision trees | Decision trees · Synthetic LFN and SFN | mean k_tau | Helped |
Closest papers by meaning
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- Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization (NeurIPS 2021 · similarity 0.88)
- Predicting Label Distribution from Multi-label Ranking (NeurIPS 2022 · similarity 0.88)
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