On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity
Authors: Chao Wang, Xin HE, Yuwen Wang, Junhui Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments further support our theoretical findings. The Python code for reproducing the numerical experiments is available on Github. |
| Researcher Affiliation | Academia | Chao Wang , Xin He , Yuwen Wang , Junhui Wang School of Statistics and Management, Shanghai University of Finance and Economics Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai Chinese University of Hong Kong wang.chao@stu.sufe.edu.cn, he.xin17@mail.shufe.edu.cn wangyw@link.cuhk.edu.hk, junhuiwang@cuhk.edu.hk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The Python code for reproducing the numerical experiments is available on Github. |
| Open Datasets | Yes | We apply both TKM and KM with check loss to the wine quality dataset, which is available in the UCI Machine Learning Repository. |
| Dataset Splits | Yes | The parameters γ and r are tuned by 5-fold cross-validation. |
| Hardware Specification | Yes | All experiments were conducted on the same hardware setup: Intel i9 13900K CPU @ 2.20GHz with 128 GB memory. |
| Software Dependencies | No | The paper mentions general software like "Python code" but does not specify library names with version numbers. |
| Experiment Setup | Yes | The data generating scheme is repeated for 50 times and all the tuning parameters are tuned to the best for both methods. |