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.
Comparison-Based Random Forests
Authors: Siavash Haghiri, Damien Garreau, Ulrike Luxburg
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,410 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,531 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a set of comprehensive experiments, we then demonstrate that the proposed random forest is efficient both for classification and regression. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of T ubingen, Germany 2Max Planck Institute for Intelligent Systems, T ubingen, Germany. |
| Pseudocode | Yes | Algorithm 1 Comp Tree(S, n0): Supervised comparison tree construction |
| Open Source Code | No | The paper provides a link for a third-party tool (TSTE) used, but no explicit statement or link to the authors' own comparison-based random forest code is provided. |
| Open Datasets | Yes | MNIST (Le Cun et al., 1998) and Gisette are handwritten digit datasets. Isolet and UCIHAR are speech recognition and human activity recognition datasets respectively (Lichman, 2013). |
| Dataset Splits | Yes | We perform 10-fold cross-validation over n0 {1, 4, 16, 64} and M {1, 4, 16, 64, 256}. Since the regression datasets have no separate training and test set, we assign 90% of the items to the training and the remaining 10% to the test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify the versions of any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | We perform 10-fold cross-validation over n0 {1, 4, 16, 64} and M {1, 4, 16, 64, 256}. |