Research map / Learning theory and algorithms / Kernels and regression theory
Multiresolution Kernel Approximation for Gaussian Process Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use MKA instead of the full GP | Gaussian process regression · housing | SMSE: 0.36 -> 0.52 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · rupture | SMSE: 0.17 -> 0.32 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · wine | SMSE: 0.59 -> 0.70 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · pageblocks | SMSE: 0.44 -> 0.63 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · compAct | SMSE: 0.58 -> 0.60 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · pendigit | SMSE: 0.15 -> 0.30 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · housing | MNLP: -0.32 -> -0.32 | No clear effect |
| use MKA instead of the full GP | Gaussian process regression · rupture | MNLP: -0.89 -> -0.54 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · wine | MNLP: -0.33 -> -0.23 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · pageblocks | MNLP: -1.10 -> -0.85 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · compAct | MNLP: -0.66 -> -0.32 | Hurt |
| use MKA instead of the full GP | Gaussian process regression · pendigit | MNLP: -0.73 -> -0.42 | Hurt |
Closest papers by meaning
- How Good Are Low-Rank Approximations in Gaussian Process Regression? (AAAI 2022 · similarity 0.89)
- Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning (ICML 2022 · similarity 0.88)
- On the Identifiability and Interpretability of Gaussian Process Models (NeurIPS 2023 · similarity 0.87)
- Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition (ICML 2021 · similarity 0.87)
- KernelMatmul: Scaling Gaussian Processes to Large Time Series (AAAI 2025 · similarity 0.86)
- Solving High Frequency and Multi-Scale PDEs with Gaussian Processes (ICLR 2024 · similarity 0.84)
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