Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Structured Gaussian process models
Models nonstationary covariances, mixtures and latent structure with identifiability analysis.
39 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Uncertainty and Gaussian processes, keyword cluster “gaussian processes · gp · kernel”
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Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
7 papers in 2023–24 and 4 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 2.6% to 1.3%.
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
- 2016 · RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition (CVPR 2016 · 61 citations)
- 2017 · Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes (NeurIPS 2017 · 22 citations)
- 2018 · Heterogeneous Multi-output Gaussian Process Prediction (NeurIPS 2018 · 48 citations)
- 2019 · Multi-resolution Multi-task Gaussian Processes (NeurIPS 2019 · 18 citations)
- 2020 · Deep Gaussian Markov Random Fields (ICML 2020 · 4 citations)
- 2021 · Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed (ICML 2021 · 28 citations)
- 2022 · Scalable Deep Gaussian Markov Random Fields for General Graphs (ICML 2022 · 2 citations)
- 2023 · Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification (ICML 2023 · 2 citations)
- 2024 · Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · 0 citations)
- 2025 · Squared families are useful conjugate priors (NeurIPS 2025 · 0 citations)
- 2026 · Transformed Latent Variable Multi-Output Gaussian Processes (ICML 2026 · 0 citations)
Earliest papers here
- RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition (CVPR 2016 · 61 citations)
- Near-Optimal Active Learning of Multi-Output Gaussian Processes (AAAI 2016 · 25 citations)
- DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays (AAAI 2016 · 10 citations)
What has been tried
35 papers here report 326 measured changes (a component added, removed or swapped, compared with a baseline): helped 65%, no clear effect 12%, hurt 17%, mixed 6%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Use a diagonal CP core instead of the dense Tucker core | US-TEMP (R=3) · RMSE | 1.255±0.108 -> 0.805±0.06 | Helped | Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · 0 citations) |
| add the idiographic kernel | Simulation · test accuracy | 0.790 ± 0.008 -> 0.942 ± 0.002 | Helped | Idiographic Personality Gaussian Process for Psychological Assessment (NeurIPS 2024 · 0 citations) |
Recent preprints
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Related approaches
Same keyword cluster
- Sparse variational Gaussian processes (92 papers)
- PAC-Bayes and decoupled GP learning (83 papers)
- Infinite-width networks as Gaussian processes (49 papers)
Elsewhere in the topic
- Nonparametric and PAC-Bayes Bayesian learning (130 papers)
- Simulation-based inference (118 papers)
- Priors and functional variational uncertainty (112 papers)
- Variational Bayesian deep learning (105 papers)
- Approximate inference in Bayesian neural networks (93 papers)
- Epistemic versus aleatoric decomposition (75 papers)
- Single-model predictive uncertainty (74 papers)
- Bayesian view of meta-learning (63 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).