Research map / Learning theory and algorithms / Kernels and regression theory
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · Wine | test RMSE: 0.727 -> 0.723 | Helped |
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · Insurance | test RMSE: 0.231 -> 0.232 | Hurt |
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · CT location | test RMSE: 3.68 -> 3.72 | Hurt |
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · Forest | test RMSE: 1.08 -> 1.05 | Helped |
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · Wine | training time: 3 sec -> 15 sec | Hurt |
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · Insurance | training time: 2 sec -> 6 sec | Hurt |
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · CT location | training time: 1 min -> 8.5 min | Hurt |
| Use recursive adaptive sampling rather than single-round row-norm sampling (ablation) | Gaussian-kernel approximate KRR · Forest | training time: 2.5 min -> 7 min | Hurt |
Closest papers by meaning
- Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time (ICML 2022 · similarity 0.89)
- Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? (NeurIPS 2017 · similarity 0.88)
- Nyström Kernel Mean Embeddings (ICML 2022 · similarity 0.87)
- Sparse Dimensionality Reduction Revisited (ICML 2024 · similarity 0.87)
- Random features for Grassmannian kernel approximation with bounded rank-one projections (NeurIPS 2026 · similarity 0.87)
- Supervised Kernel Thinning (NeurIPS 2024 · similarity 0.85)
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