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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Adaptive Minimax Regression Estimation over Sparse $\ell_q$-Hulls
Authors: Zhan Wang, Sandra Paterlini, Fuchang Gao, Yuhong Yang
JMLR 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our focus in this work is of a theoretical nature to provide an understanding of the fundamental theoretical issues about โq-aggregation or linear regression under โq-constraints. Computational aspects will be studied in the future. |
| Researcher Affiliation | Collaboration | Zhan Wang EMAIL Fulcrum Analytics Fair๏ฌeld, CT 06824, USASandra Paterlini EMAIL Department of Finance & Accounting EBS Universit at f ur Wirtschaft und Recht Gustav-Stresemann-Ring 3 65189 Wiesbaden, GermanyFuchang Gao EMAIL Department of Mathematics University of Idaho Moscow, ID 83844, USA Yuhong Yang EMAIL School of Statistics University of Minnesota 313 Ford Hall 224 Church Street Minneapolis, MN 55455, USA |
| Pseudocode | No | The paper describes algorithms and strategies in textual paragraphs and mathematical formulations, but does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | Our focus in this work is of a theoretical nature to provide an understanding of the fundamental theoretical issues about โq-aggregation or linear regression under โq-constraints. Computational aspects will be studied in the future. There is no mention of code being released, nor any links to repositories provided. |
| Open Datasets | No | The paper discusses regression models with 'i.i.d. observations' but does not specify any particular dataset used for experiments or provide access information for any dataset. The paper is theoretical in nature and does not conduct empirical studies. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using specific datasets, therefore, no information about training/test/validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not present experimental results, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical derivations and rates of convergence, without detailing any experimental setup or hyperparameters. |