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 [1].
Model Averaging Is Asymptotically Better Than Model Selection For Prediction
Authors: Tri M. Le, Bertrand S. Clarke
JMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We compare the performance of six model average predictors Mallows model averaging, stacking, Bayes model averaging, bagging, random forests, and boosting to the components used to form them. In all six cases we identify conditions under which the model average predictor is consistent for its intended limit and performs as well or better than any of its components asymptotically. This is well known empirically, especially for complex problems, although theoretical results do not seem to have been formally established. |
| Researcher Affiliation | Academia | Tri M. Le le EMAIL Department of Science, Mathematics, and Informatics Mercer University, USA Bertrand Clarke EMAIL Department of Statistics University of Nebraska-Lincoln, USA 68583-0963 |
| Pseudocode | No | The paper focuses on theoretical comparisons of model averaging techniques and presents theorems and proofs. It does not contain any structured pseudocode blocks or algorithms. |
| Open Source Code | No | The paper is theoretical and focuses on mathematical properties and proofs for model averaging techniques. It does not mention the release of any source code or provide links to code repositories. |
| Open Datasets | No | The paper presents a theoretical analysis of model averaging techniques. It does not use or refer to any specific datasets for empirical evaluation, hence no access information for datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments using datasets. Therefore, no information about dataset splits (training, validation, test) is provided. |
| Hardware Specification | No | The paper presents a theoretical analysis and does not describe any computational experiments. Consequently, there are no details provided regarding the hardware specifications used. |
| Software Dependencies | No | The paper is theoretical and does not involve computational implementations or experiments that would require specific software dependencies. Therefore, no software versions or libraries are mentioned. |
| Experiment Setup | No | The paper is theoretical, presenting mathematical theorems and proofs for model averaging. It does not describe any practical experimental setup, hyperparameters, or training configurations. |