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..
SGLB: Stochastic Gradient Langevin Boosting
Authors: Aleksei Ustimenko, Liudmila Prokhorenkova
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also empirically show that SGLB outperforms classic gradient boosting when applied to classification tasks with 0-1 loss function, which is known to be multimodal. and Our experiments on synthetic and real datasets show that SGLB outperforms standard SGB and can optimize globally such non-convex losses as 0-1 loss... |
| Researcher Affiliation | Collaboration | 1Yandex, Moscow, Russia 2Moscow Institute of Physics and Technology, Moscow, Russia 3HSE University, Moscow, Russia. |
| Pseudocode | Yes | Algorithm 1 SGB and Algorithm 2 SGLB |
| Open Source Code | Yes | The proposed algorithm is implemented within the Cat Boost open-source gradient boosting library (option langevin=True) (Cat Boost, 2020). and Our implementation of SGLB is available within the open-source Cat Boost gradient boosting library. |
| Open Datasets | Yes | The datasets are described in Table 1 of the supplementary materials. |
| Dataset Splits | Yes | We split each dataset into train, validation, and test sets in proportion 65/15/20. |
| Hardware Specification | No | The paper does not specify the hardware used for the experiments. |
| Software Dependencies | No | Our implementation of SGLB is available within the open-source Cat Boost gradient boosting library. (No version specified for CatBoost or other dependencies). |
| Experiment Setup | Yes | We set learning rate to 0.1 and ς = 10 1 for SLA. For SGLB, we set β = 103 and γ = 10 3. Moreover, we set the subsampling rate of SGB to 0.5. and For all algorithms, the maximal number of trees is set to 1000. |