Zipper: Addressing Degeneracy in Algorithm-Agnostic Inference
Authors: Geng Chen, Yinxu Jia, Guanghui Wang, Changliang Zou
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 gengchen.stat@gmail.com, yxjia@mail.nankai.edu.cn, ghwang.nk@gmail.com, zoucl@nankai.edu.cn |
| 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. |