Research map / Learning theory and algorithms / Kernels and regression theory
Sparse Shrunk Additive Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Sparse shrunk additive model (SSAM) with pairwise shrunk features and L1 on coefficients, versus SALSA (no sparsity) | additive model for regression · Real data (8 UCI/benchmark sets) | average MSE | Mixed |
| Sparse shrunk additive model (SSAM) versus COSSO for feature selection on synthetic data | feature selection · Synthetic data I and II (Precision@tau) | Precision@tau | Helped |
| SSAM with 20,000 samples in high-dimensional synthetic setting | feature selection · Simulated data (20000 samples, n=10) | Precision@tau | No clear effect |
| SSAM versus RMR on real data | regression prediction · Real data (Table 5) | average MSE | Mixed |
Closest papers by meaning
- Group Additive Structure Identification for Kernel Nonparametric Regression (NeurIPS 2017 · similarity 0.87)
- Wavelet regression and additive models for irregularly spaced data (NeurIPS 2018 · similarity 0.87)
- Tilted Sparse Additive Models (ICML 2023 · similarity 0.86)
- Feature Adaptation for Sparse Linear Regression (NeurIPS 2023 · similarity 0.86)
- Group Sparse Additive Machine (NeurIPS 2017 · similarity 0.86)
- Minimax-Optimal Univariate Function Selection in Sparse Additive Models: Rates, Adaptation, and the Estimation-Selection Gap (NeurIPS 2025 · similarity 0.85)
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