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].
High-Dimensional L2-Boosting: Rate of Convergence
Authors: Ye Luo, Martin Spindler, Jannis Kueck
JMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present simulation studies to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting. In these simulation studies, L2-Boosting clearly outperforms Lasso. |
| Researcher Affiliation | Academia | Ye Luo EMAIL Hong Kong University Business School The University of Hong Kong Hong Kong; Martin Spindler EMAIL Institute for Statistics University of Hamburg Germany; Jannis Kueck EMAIL D usseldorf Institute for Competition Economics Heinrich Heine University D usseldorf Germany |
| Pseudocode | Yes | Algorithm 1 (L2-Boosting (BA/PGA)); Algorithm 2 (restricted L2-Boosting Algorithm (res BA/res PGA)); Algorithm 3 (Orthogonal L2-Boosting (o BA)) |
| Open Source Code | No | The boosting procedures were implemented by the authors and the code is available upon request. |
| Open Datasets | Yes | The data set has been provided by DSM (Kaiserburg, Switzerland) and was made publicly available for academic research in B uhlmann et al. (2014) (Supplemental Material). |
| Dataset Splits | Yes | The size of the training set was 60 and the remaining 11 observations were used for forecasting. |
| Hardware Specification | No | The text does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | The simulations were performed in R (R Core Team (2014)). For Lasso estimation the packages hdm by Chernozhukov et al. (2015) and glmnet by Jerome Friedman (2010) (for cross-validation) were used. |
| Experiment Setup | Yes | The goal of this section is to give an illustration of the different stopping criteria. We employ the following data generating process (dgp): y = 5x1 + 2x2 + 1x3 + 0x4 + . . . + 0x10 + ε, (6) where ε N(0, 22) and X = (X1, . . . , X10) N10(0, I10) with I10 denoting the identity matrix of size 10 10. |