Research map / Learning theory and algorithms / Kernels and regression theory
Kernel ridge regression analysis
Characterize learning curves and test error of kernel ridge regression.
57 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2017–2026) · an approach in Kernels and regression theory, keyword cluster “ridge · linear regression · regularization”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
21 papers in 2023–24 and 14 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 8.5% to 5.7%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2017 · Generalization Properties of Learning with Random Features (NeurIPS 2017 · 184 citations)
- 2018 · An Iterative, Sketching-based Framework for Ridge Regression (ICML 2018 · 25 citations)
- 2019 · Non-Asymptotic Uniform Rates of Consistency for k-NN Regression (AAAI 2019 · 24 citations)
- 2020 · A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent (NeurIPS 2020 · 40 citations)
- 2021 · Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime (NeurIPS 2021 · 18 citations)
- 2022 · Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data (ICML 2022 · 2 citations)
- 2023 · On the Saturation Effect of Kernel Ridge Regression (ICLR 2023 · 2 citations)
- 2024 · Provable and Efficient Dataset Distillation for Kernel Ridge Regression (NeurIPS 2024 · 3 citations)
- 2025 · Distributionally Robust Active Learning for Gaussian Process Regression (ICML 2025 · 0 citations)
- 2026 · To Grok Grokking: Provable Grokking in Ridge Regression (ICML 2026 · 0 citations)
Earliest papers here
- Generalization Properties of Learning with Random Features (NeurIPS 2017 · 184 citations)
- Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging (ICML 2017 · 50 citations)
- An Iterative, Sketching-based Framework for Ridge Regression (ICML 2018 · 25 citations)
What has been tried
42 papers here report 278 measured changes (a component added, removed or swapped, compared with a baseline): helped 55%, no clear effect 15%, hurt 19%, mixed 10%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Remove the uncertainty constraint from constrained DR variance reduction, yielding DR variance reduction | Synthetic datasets · worst-case expected squared error E_T | Helped | Distributionally Robust Active Learning for Gaussian Process Regression (ICML 2025 · 0 citations) |
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 35 papers; from OpenAlex and the papers' affiliations).
- University of California, Berkeley · 7 papers
- Tsinghua University · 3 papers
- Toyota Technological Institute at Chicago · 3 papers
- University of Basel · 3 papers
- McMaster University · 3 papers
- University of Chicago · 2 papers
- Princeton University · 2 papers
- UniDistance Suisse · 2 papers
Use it
Download BibTeX of 39 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- High-dimensional asymptotic generalization (82 papers)
- High-dimensional linear regression theory (61 papers)
- Double descent and overfitting (53 papers)
Elsewhere in the topic
- RKHS kernel methods (127 papers)
- Neural tangent kernel analysis (109 papers)
- Statistical perspectives on generalization (99 papers)
- Information-theoretic generalization bounds (94 papers)
- Kernel regression under shift (89 papers)
- Generalization theory of deep networks (67 papers)
- Empirical generalization measures (61 papers)
- PAC-Bayes bounds (39 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).