Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Gradient Boosting Performs Gaussian Process Inference
Authors: Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments confirm that the proposed sampler from the Gaussian process posterior outperforms the previous approaches (Malinin et al., 2021) and gives better knowledge uncertainty estimates and improved out-of-domain detection. |
| Researcher Affiliation | Collaboration | Aleksei Ustimenko Share Chat EMAIL Artem Beliakov Yandex Research, HSE University EMAIL Liudmila Prokhorenkova Yandex Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Sample Tree(r; m, n, β), Algorithm 2 Train GBDT(z; ϵ, T, m, n, β, λ), Algorithm 3 Sample Prior(T, m, n), Algorithm 4 Sample Posterior(z; ϵ, T1, T0, m, n, β, σ, δ) |
| Open Source Code | Yes | The code of our experiments can be found on Git Hub.7 https://github.com/Take Over/Gradient-Boosting-Performs-Gaussian-Process-Inference |
| Open Datasets | Yes | For the experiments, we use several standard regression datasets (Gal & Ghahramani, 2016). |
| Dataset Splits | Yes | As a hyperparameter (that is estimated on the validation set), we consider β {10 2, 10 1, 1}., Here we perform cross-validation to estimate statistical significance with paired t-test and highlight the approaches that are insignificantly different from the best one (p-value > 0.05). |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. It only mentions 'Implementation details' in Appendix H, which does not include hardware specifications. |
| Software Dependencies | No | The paper mentions using 'the standard Cat Boost library' but does not specify a version number for it or any other software dependencies, which is required for a reproducible description. |
| Experiment Setup | Yes | For KGB, we fix ϵ = 0.3, T0 = 100, T1 = 900, σ = 10 2, δ = 10 4 β = 0.1, m = 4, n = 64, and sampled 100 KGB models., As a hyperparameter (that is estimated on the validation set), we consider β {10 2, 10 1, 1}., Finally, we set l2 leaf reg value to 0, as SGLB does. |