Research map / Learning theory and algorithms / Matrix and tensor methods
Moving Window Regression: A Novel Approach to Ordinal Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use local rho-regressors instead of only the global rho-regressor (ablation) | MWR · MORPH II setting A | MAE: 2.24 -> 2.13 | Helped |
| use local rho-regressors instead of only the global rho-regressor (ablation) | MWR · CLAP2015 test | MAE: 2.82 -> 2.77 | Helped |
| use local rho-regressors instead of only the global rho-regressor (ablation) | MWR · UTK | MAE: 4.49 -> 4.37 | Helped |
| select reference pairs with minimum gamma instead of random selection (ablation) | MWR · CLAP2015 test | MAE: 2.82±0.01 -> 2.77 | Helped |
| select reference pairs with maximum gamma instead of random selection (ablation) | MWR · CLAP2015 test | MAE: 2.82±0.01 -> 3.17 | Hurt |
| use a geometric rather than arithmetic search window (ablation) | global MWR · CLAP2015 test | MAE: 3.21 -> 2.93 | Helped |
| set geometric tau to 0.1 rather than 0.05 (ablation) | global MWR · CLAP2015 test | MAE: 3.03 -> 2.93 | Helped |
| set geometric tau to 0.1 rather than 0.125 (ablation) | global MWR · CLAP2015 test | MAE: 2.95 -> 2.93 | Helped |
Closest papers by meaning
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- GoR: A Unified and Extensible Generative Framework for Ordinal Regression (ICLR 2026 · similarity 0.85)
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- SLACE: A Monotone and Balance-Sensitive Loss Function for Ordinal Regression (AAAI 2025 · similarity 0.82)
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