Research map / Learning theory and algorithms / Kernels and regression theory
How Good Are Low-Rank Approximations in Gaussian Process Regression?
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Mercer truncation instead of random Fourier features at the same rank | GP regression · Seven UCI datasets | NLPD | Helped |
| Use Mercer truncation instead of sparse GP regression | GP regression · Seven UCI datasets | NLPD | Mixed |
| Increase rank from 6 to 300 | FGP and MGP · Seven UCI datasets | NLPD | Helped |
| Use Mercer truncation rather than random Fourier features at fixed rank | Gaussian-kernel GP · Simulated data | KL divergence | Helped |
| Use Mercer truncation for a Gaussian kernel rather than a Matérn kernel | GP regression · Simulated data | KL divergence | Helped |
| Use fewer Mercer components (lower rank) rather than more components | Mercer GP · Real-data benchmarks | NLPD | Mixed |
Closest papers by meaning
- Multiresolution Kernel Approximation for Gaussian Process Regression (NeurIPS 2017 · similarity 0.89)
- Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning (ICML 2022 · similarity 0.88)
- Parametric Gaussian Process Regressors (ICML 2020 · similarity 0.88)
- Bezier Gaussian Processes for Tall and Wide Data (NeurIPS 2022 · similarity 0.88)
- Log-Linear-Time Gaussian Processes Using Binary Tree Kernels (NeurIPS 2022 · similarity 0.88)
- Robust and Conjugate Gaussian Process Regression (ICML 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.