Research map / Learning theory and algorithms / Kernels and regression theory
Simplex Random Features
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace ORFs with simplex coupling (SimRFs) | PRF kernel estimator for Gaussian kernel · analytic MSE, d=64 | MSE vs ORF | Helped |
| weight-dependent coupling (SimRFs+) instead of SimRFs (ablation) | nonparametric classification, m=d · UCI datasets (8) | classification accuracy: e.g. banknote -0.0097, wifi +0.0159 | Mixed |
| replace ORFs with SimRFs in Performer attention | ViT on ImageNet2012 · ImageNet2012 accuracy | accuracy: +0.5% | Helped |
| replace ORFs with SimRFs in Performer attention | ViT on other datasets · Fashion-MNIST, iNaturalist2021, Places365 | accuracy | No clear effect |
Closest papers by meaning
- Hybrid Random Features (ICLR 2022 · similarity 0.89)
- Chefs' Random Tables: Non-Trigonometric Random Features (NeurIPS 2022 · similarity 0.89)
- Variance-Reducing Couplings for Random Features (ICLR 2025 · similarity 0.87)
- Manifold Random Features (NeurIPS 2026 · similarity 0.87)
- Random features for Grassmannian kernel approximation with bounded rank-one projections (NeurIPS 2026 · similarity 0.85)
- General Graph Random Features (ICLR 2024 · similarity 0.85)
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