On Computing Optimal Tree Ensembles
Authors: Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |