Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exact Post Model Selection Inference for Marginal Screening
Authors: Jason Lee, Jonathan E Taylor
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 7, we evaluate the methodology on two real datasets. In Figure 1, we have already seen that the confidence intervals constructed using Algorithm 2 have exactly 1 α coverage proportion. In this section, we perform two experiments on real data where the linear model does not hold, the noise is not Gaussian, and the noise variance is unknown. |
| Researcher Affiliation | Academia | Jason D. Lee Computational and Mathematical Engineering Stanford University Stanford, CA 94305 EMAIL Jonathan E. Taylor Department of Statistics Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | Yes | Algorithm 1 Marginal screening algorithm |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The diabetes dataset contains n = 442 diabetes patients measured on p = 10 baseline variables [6]. |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits with percentages, sample counts, or references to predefined standard splits for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or cloud computing resources) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | The confidence intervals were constructed for the k = 2 variables selected by the marginal screening algorithm. The z-test intervals were constructed via (4) with α = .1... |