Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Towards Convergence Rate Analysis of Random Forests for Classification
Authors: Wei Gao, Zhi-Hua Zhou
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,256 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,718 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a pure theoretical work without particular application foreseen. |
| Researcher Affiliation | Academia | Wei Gao Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing, 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1 A simplified variant of Breiman s original random tree [12] |
| Open Source Code | No | The paper does not mention releasing any source code or provide links to a repository for the described methodology. |
| Open Datasets | No | The paper discusses theoretical properties based on 'training data Sn' and an 'unknown underlying distribution D', but it does not refer to specific, publicly available datasets used for empirical training. This is a theoretical paper. |
| Dataset Splits | No | The paper discusses theoretical analysis and does not mention specific training/validation/test splits as it performs no empirical experiments. |
| Hardware Specification | No | As a theoretical paper, no hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | As a theoretical paper, no software dependencies with version numbers are mentioned for running experiments. |
| Experiment Setup | No | As a theoretical paper, no experimental setup details like hyperparameters or training settings are provided. |