Selective inference for group-sparse linear models
Authors: Fan Yang, Rina Foygel Barber, Prateek Jain, John Lafferty
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give numerical results to illustrate these tools on simulated data and on health record data. In this section we present results from experiments on simulated and real data, performed in R [11]. |
| Researcher Affiliation | Collaboration | Fan Yang Department of Statistics University of Chicago fyang1@uchicago.edu Rina Foygel Barber Department of Statistics University of Chicago rina@uchicago.edu Prateek Jain Microsoft Research India prajain@microsoft.com John Lafferty Depts. of Statistics and Computer Science University of Chicago lafferty@galton.uchicago.edu |
| Pseudocode | Yes | See the supplementary material for detailed pseudo-code. |
| Open Source Code | Yes | Code reproducing experiments: http://www.stat.uchicago.edu/~rina/group_inf.html |
| Open Datasets | Yes | We examine the 2015 California county health data7 which was also studied by Loftus and Taylor [9]. ... 7Available at http://www.countyhealthrankings.org |
| Dataset Splits | No | The paper describes generating simulated data and using real-world data but does not specify explicit train/validation/test splits, percentages, or cross-validation details for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory specifications, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper only mentions 'performed in R [11]' without specifying versions of R or any other software libraries or packages with their version numbers. |
| Experiment Setup | Yes | We run IHT to select k = 10 groups over T = 5 iterations, with step sizes ηt = 2 and initial point β0 = 0. |