Transfer Learning for High-dimensional Quantile Regression with Statistical Guarantee
Authors: Sheng Qiao, Yong He, Wenxin Zhou
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough simulation studies justify our theoretical analysis. |
| Researcher Affiliation | Academia | Sheng Qiao EMAIL Department of Mathematics University of California San Diego San Diego, CA 92093, USA Yong He EMAIL Institute for Financial Studies Shandong University Jinan, 250100, China Wen-Xin Zhou EMAIL Department of Information and Decision Sciences University of Illinois at Chicago Chicago, IL 60607, USA |
| Pseudocode | Yes | Algorithm 1: Oracle ℓ1-Trans-SQR Algorithm 2: Transferable Source Detection Algorithm 3: Trans-SQR Algorithm 4: Oracle Trans-SQR with ℓ0-norm constrained transferring set |
| Open Source Code | No | The paper does not contain any explicit statements about providing open-source code, nor does it include links to code repositories. |
| Open Datasets | No | The paper conducts numerical studies using simulated data, explicitly describing the data generation process (e.g., "The covariates from target x(0) i are i.i.d. Gaussian with mean zero and covariance matrix Σ...", "ϵ(0) i are i.i.d. Gaussian with mean zero and variance one"). It does not use or provide access information for any pre-existing public datasets. |
| Dataset Splits | No | The numerical studies are based on simulated data, and while Algorithm 2 describes partitioning target data into 'q' subsets for transferable source detection (a form of cross-validation), the paper does not specify fixed training/test/validation splits for a specific dataset (either real or generated) in the traditional sense required for reproducing the main model evaluation. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments or simulations. |
| Software Dependencies | No | The paper mentions statistical methods and estimators (e.g., "convolution-type smoothed quantile regression", "iteratively reweighted ℓ1-penalized SQR estimator") but does not list any specific software libraries, frameworks, or programming languages with version numbers used for implementation. |
| Experiment Setup | Yes | We consider p = 500, n0 = 200, and n1, . . . , n10 = 150. The covariates from target x(0) i are i.i.d. Gaussian with mean zero and covariance matrix Σ with Σjj = 0.5|j j |... ϵ(0) i are i.i.d. Gaussian with mean zero and variance one... For the target, the true parameter β , we set s = 5, βj = 0.5 for j {1, . . . , s}, and βj = 0 otherwise. ... We take λω = Cω p log(p)/(n Am + n0), λδ = Cδ p log(p)/n0, where Cω and Cδ are sufficiently large constants... |