Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models
Authors: Jing Wang, HaiYing Wang, Hao Zhang
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct numerical experiments using both simulated and real-world data sets to demonstrate the performance of the proposed methods. |
| Researcher Affiliation | Academia | Jing Wang Department of Statistics University of Connecticut Storrs, CT 06269 EMAIL Hai Ying Wang Department of Statistics University of Connecticut Storrs, CT 06269 EMAIL Hao Helen Zhang Department of Mathematics University of Arizona EMAIL |
| Pseudocode | Yes | Algorithm 1 Poisson Subsampling algorithm |
| Open Source Code | Yes | Codes are submitted as supplement for anonymity. They will be released in a public github repository after the review period. |
| Open Datasets | Yes | (i) Covtype data set: It is available at https://archive.ics.uci.edu/ml/datasets/ covertype, with N = 581012 observations and 54 covariates... |
| Dataset Splits | Yes | We use the 5-fold cross-validation and Bayesian information criterion (BIC) to determine the tuning parameter λ for the lasso and the adaptive lasso, and choose γ = 1 for the adaptive lasso. |
| Hardware Specification | No | The paper states that codes were 'implemented on a Linux workstation' in Section 5.1.3, but does not provide specific details on the CPU, GPU models, or memory of the hardware used. |
| Software Dependencies | Yes | Our codes are written in the julia programming language [2] and implemented on a Linux workstation. The lasso pathes are solved with Lasso.jl [13]. |
| Experiment Setup | Yes | We use the 5-fold cross-validation and Bayesian information criterion (BIC) to determine the tuning parameter λ for the lasso and the adaptive lasso, and choose γ = 1 for the adaptive lasso. |