Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models
Authors: Jing Wang, HaiYing Wang, Hao Zhang
NeurIPS 2024 | Conference 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 jing.7.wang@uconn.edu Hai Ying Wang Department of Statistics University of Connecticut Storrs, CT 06269 haiying.wang@uconn.edu Hao Helen Zhang Department of Mathematics University of Arizona hzhang@math.arizona.edu |
| 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. |