Temporal Domain Generalization via Learning Instance-level Evolving Patterns
Authors: Yujie Jin, Zhibang Yang, Xu Chu, Liantao Ma
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple classification and regression benchmarks demonstrate the effectiveness of the proposed CTOT framework. |
| Researcher Affiliation | Academia | Yujie Jin1,2 , Zhibang Yang2 , Xu Chu1,2,3 and Liantao Ma4 1School of Computer Science, Peking University, Beijing, China 2Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China 3Center on Frontiers of Computing Studies, Peking University, Beijing, China 4National Engineering Research Center of Software Engineering, Peking University, Beijing, China {jinyujie, chu xu, malt}@pku.edu.cn, yangzb@stu.pku.edu.cn |
| Pseudocode | Yes | The pseudo code of the overall CTOT is provided in Appendix A.2. |
| Open Source Code | Yes | The code and appendix are both available on https://github.com/JinYujie99/CTOT. |
| Open Datasets | Yes | Following existing works [Nasery et al., 2021; Bai et al., 2023], we conduct experiments on the following five classification datasets: Rotated Moons (2-Moons), Rotated MNIST (Rot-MNIST), Online News Popularity (ONP), Shuttle, and Electrical Demand (Elec2); and the following two regression datasets: House prices dataset (House), Appliances energy prediction dataset (Appliance). |
| Dataset Splits | Yes | For tuning hyperparameters, we consider data from the last source domain (DT ) as the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory amounts. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For tuning hyperparameters, we consider data from the last source domain (DT ) as the validation set. We control the number of generated instances to be close to the number of instances in a single source domain. For each method, the experiments are repeated 5 times with different random seeds, and we report the mean results and standard deviation. More details are given in Appendix B.3 and B.4. |