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..
Zipper: Addressing Degeneracy in Algorithm-Agnostic Inference
Authors: Geng Chen, Yinxu Jia, Guanghui Wang, Changliang Zou
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finite-sample experiments demonstrate that our procedure, with a simple choice of the slider, works well across a wide range of settings. |
| Researcher Affiliation | Academia | Geng Chen Yinxu Jia Guanghui Wang Changliang Zou NITFID, School of Statistics and Data Science, LPMC, KLMDASR, and LEBPS, Nankai University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The algorithm for the proposed Zipper testing procedure |
| Open Source Code | No | The paper describes the algorithm and synthetic data generation but does not explicitly state that the source code for the methodology is released or provide a link to a repository. |
| Open Datasets | Yes | We apply the Zipper method to the widely used MNIST handwritten digit dataset [32]. We expand the application of our Zipper method to the bodyfat dataset [33], |
| Dataset Splits | No | The paper describes a K-fold cross-fitting scheme and within-fold data partitioning for the Zipper device, but does not provide specific, fixed training, validation, and test dataset splits for the entire dataset. |
| Hardware Specification | Yes | These experiments are executed on an Intel Xeon Gold 5118 CPU @ 2.30GHz. |
| Software Dependencies | No | The paper mentions software like ordinary least-squares regression, LASSO, abess algorithm, Convolutional Neural Network (CNN), and random forest, but does not specify their version numbers. |
| Experiment Setup | Yes | The significance level is chosen as α = 5%, and our experiments entail 1, 000 replications. We specify the slider parameter τ = min{τ0, 0.9} with n0 = 50 as suggested in Section 2.5. |