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..
False discovery proportion envelopes with m-consistency
Authors: Meah Iqraa, Blanchard Gilles, Roquain Etienne
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These improvements are illustrated with numerical experiments and real data examples. In particular, the improvement is significant in the knockoffs setting, which shows the impact of the method for a practical use. |
| Researcher Affiliation | Academia | Iqraa Meah EMAIL Center for Research in Epidemiology and Statistic S (CRESS) Universit e Paris Cit e and Universit e Sorbonne Paris Nord, Inserm, INRAE F-75004 Paris, France; Gilles Blanchard EMAIL Institut Math ematiques de Orsay (IMO) CNRS, Inria, Universit e Paris-Saclay F-91405 Orsay Cedex; Etienne Roquain EMAIL Laboratoire de Probabilit es, Statistique et Mod elisation, CNRS, Sorbonne Universit e, Universit e de Paris. Sorbonne Universit e 4 place Jussieu, 75005, Paris |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. Procedures are described mathematically or in narrative text. |
| Open Source Code | Yes | All our numerical experiments are reproducible from the code provided in the repository https://github.com/iqm15/Consistent FDP. |
| Open Datasets | Yes | We now consider the online case, by applying our method to the real data example coming from the International Mice Phenotyping Consortium (IMPC) (Mu noz-Fuentes et al., 2018) |
| Dataset Splits | No | The paper describes simulation setups and theoretical models (e.g., Gaussian location model, VCT model) for generating data for numerical experiments, but does not specify training/test/validation splits of any publicly available datasets in the machine learning sense. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) used for the experiments. |
| Experiment Setup | Yes | Throughout the experiments, the default value for δ is 0.25 and the default number of replications to evaluate each FDP bound is 1000. To investigate the consistency property, we take m varying in the range {10i, 2 i 6}, and we consider the FDP bounds FDP Simes α (16), FDP DKW α (17), FDP KR α (18), FDP Well α (19) for α {0.05, 0.1, 0.15, 0.2}. |