Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Selective inference for group-sparse linear models
Authors: Fan Yang, Rina Foygel Barber, Prateek Jain, John Lafferty
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,622 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,007 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |