Research map / Learning theory and algorithms / Kernels and regression theory
Recursive Sampling for the Nystrom Method
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ridge leverage score sampling instead of uniform landmark sampling | Nyström approximation · Covertype | landmarks needed for ||K - K̃||₂ ≤ 1: 3800 -> 650 | Helped |
| Use Recursive RLS-Nyström instead of random Fourier features | kernel approximation · YearPredictionMSD | total time: 265 sec. -> 515 sec. | Hurt |
| Use Recursive RLS-Nyström instead of uniform sampling | Nyström kernel approximation · YearPredictionMSD | runtime to approximation-quality tradeoff | Helped |
| Undersample at intermediate recursive calls to balance runtimes (ablation) | Recursive RLS-Nyström · kernel approximation | per-sample runtime | Helped |
| Use fixed-sample-size parameter selection instead of requiring a user-specified ridge parameter (ablation) | Recursive RLS-Nyström · kernel approximation | approximation quality at a fixed sample budget | Helped |
Closest papers by meaning
- Sampling-based Nyström Approximation and Kernel Quadrature (ICML 2023 · similarity 0.93)
- Computationally Efficient Nyström Approximation using Fast Transforms (ICML 2016 · similarity 0.90)
- Optimal Convergence Rates for Agnostic Nyström Kernel Learning (ICML 2023 · similarity 0.90)
- Fast DPP Sampling for Nystrom with Application to Kernel Methods (ICML 2016 · similarity 0.90)
- Incremental Nyström-based Multiple Kernel Clustering (AAAI 2025 · similarity 0.89)
- On Fast Leverage Score Sampling and Optimal Learning (NeurIPS 2018 · similarity 0.89)
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