Selective inference for group-sparse linear models
Authors: Fan Yang, Rina Foygel Barber, Prateek Jain, John Lafferty
NeurIPS 2016 | Venue 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 EMAIL Rina Foygel Barber Department of Statistics University of Chicago EMAIL Prateek Jain Microsoft Research India EMAIL John Lafferty Depts. of Statistics and Computer Science University of Chicago EMAIL |
| 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. |