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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 ο¬rst 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 ο¬t 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. |