Vertical Symbolic Regression via Deep Policy Gradient
Authors: Nan Jiang, Md Nasim, Yexiang Xue
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our VSR-DPG significantly outperforms popular baselines in identifying both algebraic equations and ordinary differential equations on a series of benchmarks. (1) In Table 1, our VSR-DPG attains the smallest median NMSE values in 7 out of 8 datasets, against current popular baselines including VSR-GP. (2) Further analysis on the best-discovered equation (in Table 3) shows that VSR-DPG uncovers up to 50% of the exact governing equations with 5 input variables, where the baselines only attain 0%. (3) In Table 2, our VSR-DPG can find high-quality expressions on datasets with up to 50 variables, because of the vertical discovery idea. (4) On discovery of ordinary differential equations in Table 4, our VSR-DPG also improves over current baselines.1 |
| Researcher Affiliation | Academia | Nan Jiang , Md Nasim , Yexiang Xue Department of Computer Science, Purdue University {jiang631, mnasim, yexiang}@purdue.edu |
| Pseudocode | Yes | We summarize the whole process of VSR-DPG in Algorithm 1 in the appendix. |
| Open Source Code | Yes | 1The code is at https://github.com/jiangnanhugo/VSR-DPG. |
| Open Datasets | Yes | For the dataset on algebraic expressions, we consider the 8 groups of expressions from the Trigonometric dataset [Jiang and Xue, 2023]... All of them are collected from [Brunton et al., 2016]. |
| Dataset Splits | No | The paper mentions evaluating on a 'separately-generated testing dataset' but does not provide specific train/validation/test splits (e.g., percentages or sample counts) or references to predefined splits for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments (e.g., CPU/GPU models, memory, or specific computing infrastructure). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | Yes | The detailed settings are in Appendix C.2. ... The detailed experiment configurations are in Appendix C.4. |