On the Exploration of Local Significant Differences For Two-Sample Test
Authors: Zhijian Zhou, Jie Ni, Jia-He Yao, Wei Gao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We finally conduct extensive experiments to validate the effectiveness of our proposed methods on two-sample test and the exploration of local significant differences. |
| Researcher Affiliation | Academia | Zhi-Jian Zhou, Jie Ni, Jia-He Yao, Wei Gao National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 Construction of partition tree |
| Open Source Code | No | The paper mentions downloading code for comparison methods (e.g., 'Dataset blob is downloaded from github.com/fengliu90/DK-for-TST'), but it does not provide an explicit statement or link for the open-source code of its own proposed methods. |
| Open Datasets | Yes | Dataset blob is downloaded from github.com/fengliu90/DK-for-TST, and other datasets are downloaded from www.openml.org. |
| Dataset Splits | Yes | We train on a subset of each available data, and test on 100 random subsets from the remaining dataset, and the ratio is set as 4 : 1 for training and testing. We repeat such process 10 times for each dataset. ... For our MEMa Bi D, we set α = 0.05 and take 5-fold cross validation to select β [1 : 0.2 : 2]. |
| Hardware Specification | Yes | All experiments are performed with Python on nodes of a computational cluster with a single CPU (Intel Core i9-10900X 3.7GHz) and a single GPU (Ge Force RTX 2080 Ti), running Ubuntu with 128GB main memory. |
| Software Dependencies | No | In optimization, we adapt Adam optimization method from the pytorch library in python [88, 89]. ... running Ubuntu with 128GB main memory. The paper mentions software like Python, PyTorch, Adam, and Ubuntu, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For our MEMa Bi D, we set α = 0.05 and take 5-fold cross validation to select β [1 : 0.2 : 2]. We limit the cardinality of test locations within 20 for ME and MEMa Bi D as in [9 11], and optimization parameters of Eqn. (5) is presented in Appendix C. ... Table 4 presents the details of the hyperparameter settings for each dataset, including the number of test locations, learning rate. |