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..
On Computing Optimal Tree Ensembles
Authors: Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Mainly, we provide two novel algorithms for MTES and MMAXTES3 and matching lower-bound results for their running times. We call the first one witness-tree algorithm. This algorithm demonstrates that prospects for tractable algorithms for optimizing decision trees can be non-trivially generalized to optimizing tree ensembles. |
| Researcher Affiliation | Academia | 1Fakult at f ur Mathematik und Informatik, Friedrich-Schiller-Universit at Jena, Germany 2Algorithmics and Computational Complexity, TU Berlin, Germany 3Algorithm Engineering, HU Berlin, Germany 4Institute of Logic and Computation, TU Wien, Austria. |
| Pseudocode | Yes | Algorithm 1: Computing tree ensembles. 1 Function Refine Ensemble (C, (E, λ), S) |
| Open Source Code | No | No explicit statement or link indicating the availability of open-source code for the described methodology was found. |
| Open Datasets | No | The paper uses abstract training data sets (E, λ) and does not specify any named public datasets with access information or citations. |
| Dataset Splits | No | The paper discusses theoretical aspects and algorithms for classification but does not describe empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper discusses theoretical algorithms and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper discusses theoretical algorithms and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical algorithms and their complexity. It does not describe an experimental setup with hyperparameters or system-level training settings. |