Online Symbolic Regression with Informative Query
Authors: Pengwei Jin, Di Huang, Rui Zhang, Xing Hu, Ziyuan Nan, Zidong Du, Qi Guo, Yunji Chen
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through comprehensive experiments, we show that QUOSR can facilitate modern symbolic regression methods by generating informative data. |
| Researcher Affiliation | Collaboration | 1 State Key Lab of Processors, Institute of Computing Technology, CAS 2 University of Chinese Academy of Sciences 3 Cambricon Technologies |
| Pseudocode | Yes | Algorithm 1: The query-based framework |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or a direct link to a code repository for the described methodology. |
| Open Datasets | Yes | We train QUOSR using expressions in the dataset of Valipour et al. (2021) which contains about 500k one-variable expressions. |
| Dataset Splits | Yes | For each expression, 30 data points sampled uniformly in the range of [ 3.0, 3.0] are used to generate expressions, and another 30 data points sampled uniformly in the range of [ 5.0, 3.0] [3.0, 5.0] are used to evaluate the predicted expression. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory, or specific computing clusters) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Symbolic GPT' and 'Point Net' but does not provide specific version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | The max query times K is set to 9, thus we first uniformly sample 3 points and then generate the other 27 data points in the following 9 query steps. We limit QUOSR to generate values of x [ 3.0, 3.0] to fit the range of the original dataset. For each expression, 30 data points sampled uniformly in the range of [ 3.0, 3.0] are used to generate expressions, and another 30 data points sampled uniformly in the range of [ 5.0, 3.0] [3.0, 5.0] are used to evaluate the predicted expression. |