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..
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Authors: Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted an extensive evaluation on a predefined benchmark with 19 classification datasets and demonstrate that our method outperforms existing gradient-boosting and deep learning frameworks on most datasets. |
| Researcher Affiliation | Academia | Sascha Marton University of Mannheim, Germany EMAIL Stefan L udtke University of Rostock, Germany EMAIL Christian Bartelt University of Mannheim, Germany EMAIL Heiner Stuckenschmidt University of Mannheim, Germany EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The method is available under: https://github.com/s-marton/GRANDE |
| Open Datasets | Yes | For our evaluation, we used a predefined collection of datasets that was selected based on objective criteria from Open ML Benchmark Suites and comprises a total of 19 binary classification datasets (see Table 5 for details). The selection process was adopted from Bischl et al. (2021) |
| Dataset Splits | Yes | Furthermore, we report the mean and standard deviation of the test performance over a 5-fold cross-validation to ensure reliable results. |
| Hardware Specification | Yes | For all methods, we used a single NVIDIA RTX A6000. |
| Software Dependencies | No | The paper mentions using Optuna for hyperparameter optimization and frameworks like XGBoost and Cat Boost, but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For GRANDE, we used a batch size of 64 and early stopping after 25 epochs. Similar to NODE Popov et al. (2019), GRANDE uses an Adam optimizer with stochastic weight averaging over 5 checkpoints (Izmailov et al., 2018) and a learning rate schedule that uses a cosine decay with optional warmup (Loshchilov & Hutter, 2016). We optimized the hyperparameters using Optuna (Akiba et al., 2019) with 250 trials... |