A Difference Standardization Method for Mutual Transfer Learning
Authors: Haoqing Xu, Meng Wang, Beilun Wang
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the method s performance using both synthetic and real-world data. Compared to previous methods, Diff S demonstrates a speed-up of approximately 3000 times that of similar methods and achieves the same accurate learnability structure estimation. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China. |
| Pseudocode | Yes | Algorithm 1 Diff S ... Algorithm 2 Learnability Structure Recovering Ψ |
| Open Source Code | No | The paper states "We implement Diff S by Matlab, consistent with other baselines" but does not provide any specific link to source code or explicitly state that the code is publicly available. |
| Open Datasets | Yes | We apply Diff S on the NOAA n Clim Div Database1 (Vose et al., 2014) and Microarray Data (Wille et al., 2004). 1https://www.ncei.noaa.gov/access/ metadata/landing-page/bin/iso?id=gov.noaa. ncdc:C00005 |
| Dataset Splits | Yes | All M domains are randomly splited into training set, validation set and test set, with the proportion of 7 : 1 : 2. |
| Hardware Specification | Yes | We implement Diff S by Matlab, consistent with other baselines, and conduct experiments on a Linux server with an Intel Xeon Bronze 3204 CPU with up to 12 threads and 32Gi B memory. |
| Software Dependencies | No | The paper states "We implement Diff S by Matlab", but it does not specify a version number for Matlab or any other software dependencies. |
| Experiment Setup | Yes | In the base case settings, we set the number of domains M = 50, the number of subgroups S = 5, the sample size n = 100, the dimension p = q = 10. ... We set ν = 0.001 for Diff S in all cases. For CD Fusion, the BIC tuning procedure optimizes the parameters for 5 times... For k-means, we vary k from 1 to M and choose the best k with the least BIC value. |