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 [1].
On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity
Authors: Chao Wang, Xin HE, Yuwen Wang, Junhui Wang
NeurIPS 2024 | Venue PDF | 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 EMAIL, EMAIL EMAIL, EMAIL |
| 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. |