Research map / Learning theory and algorithms / Kernels and regression theory
Stable Learning via Sparse Variable Independence
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine sample reweighting with sparse variable selection | SVI · Synthetic linear settings | Mean_Error: 0.435 -> 0.356 (n=1000, r=2.5); 0.411 -> 0.369 (n=1500, r=2.5); 0.443 -> 0.330 (n=1500, r=3.0) | Helped |
| Use density-ratio reweighting with sparse variable selection | NonlinearSVI · Synthetic nonlinear settings | Mean_Error: 0.177 -> 0.130 (n=15000, r=2.0); 0.176 -> 0.125 (n=20000, r=2.0); 0.208 -> 0.126 (n=25000, r=2.2) | Helped |
| Add iterative feedback between reweighting and sparse learning rather than running one iteration (ablation) | SVI · Synthetic linear settings | Mean_Error: 0.374 -> 0.356 (n=1000, r=2.5); 0.388 -> 0.369 (n=1500, r=2.5); 0.380 -> 0.357 (n=2000, r=2.5); 0.356 -> 0.345 (r=2.0); | Helped |
| Use iterative feedback to preserve stable-variable correlations while reducing stable–unstable correlations | SVI · Synthetic data | effective sample size | Helped |
Closest papers by meaning
- A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization (ICML 2022 · similarity 0.90)
- Covariate-Shift Generalization via Random Sample Weighting (AAAI 2023 · similarity 0.87)
- Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift (ICML 2024 · similarity 0.85)
- Sufficient Invariant Learning for Distribution Shift (CVPR 2025 · similarity 0.84)
- Differentially Private High-dimensional Variable Selection via Integer Programming (NeurIPS 2025 · similarity 0.84)
- On Sparse Canonical Correlation Analysis (NeurIPS 2024 · similarity 0.84)
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