Research map / Learning theory and algorithms / Sample complexity
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use direct comparison queries instead of label-induced comparisons | AirBnB worker feedback, Task 1 · AirBnB price, Task 1 | Error: 41.3% -> 31.3% | Helped |
| Use direct comparison queries instead of label-induced comparisons | AirBnB worker feedback, Task 2 · AirBnB price, Task 2 | Disagreement: 29.5% -> 16.4% | Helped |
| Collect direct comparisons rather than direct labels | AirBnB worker feedback · AirBnB price | Average time: 63s -> 64s | No clear effect |
| Combine comparison feedback with direct labels using R² | R² nonparametric regression · AirBnB price, Task 1 and Task 2 | Prediction error | Helped |
| Combine comparison feedback with direct labels using R² | R² nonparametric regression · APPA-REAL age prediction | Prediction error | Helped |
| Use more than one nearest neighbor in the final R² prediction step (ablation) | R² nonparametric regression · Experiments | Prediction error | Helped |
| Use feature-structured rankSVM scores instead of nearest-neighbor Borda-count scores (ablation) | R² with pairwise comparisons · Synthetic data and AirBnB price | Prediction error | Mixed |
| Augment CLR's regression features with the estimated projection (ablation) | CLR · Linear regression experiments | Prediction error | Helped |
| Use active rather than passive comparison queries | CLR · Simulated linear regression | Prediction error | Helped |
| Use active rather than passive comparison queries | CLR · AirBnB price, Task 1 | Prediction error | No clear effect |
| Use active rather than passive comparison queries | CLR · AirBnB price, Task 2 | Prediction error | Helped |
Closest papers by meaning
- SLACE: A Monotone and Balance-Sensitive Loss Function for Ordinal Regression (AAAI 2025 · similarity 0.89)
- Label Ranking through Nonparametric Regression (ICML 2022 · similarity 0.87)
- Leaf-Smoothed Hierarchical Softmax for Ordinal Prediction (AAAI 2018 · similarity 0.87)
- GoR: A Unified and Extensible Generative Framework for Ordinal Regression (ICLR 2026 · similarity 0.86)
- Soft Labels for Ordinal Regression (CVPR 2019 · similarity 0.86)
- Robust Ordinal Embedding from Contaminated Relative Comparisons (AAAI 2019 · similarity 0.86)
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