Transformer-based Planning for Symbolic Regression
Authors: Parshin Shojaee, Kazem Meidani, Amir Barati Farimani, Chandan Reddy
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various datasets show that our approach outperforms state-of-the-art methods, enhancing the model s fitting-complexity trade-off, extrapolation abilities, and robustness to noise. |
| Researcher Affiliation | Academia | Parshin Shojaee 1 , Kazem Meidani 2, Amir Barati Farimani 2,3 , Chandan K. Reddy1 1 Department of Computer Science, Virginia Tech 2 Department of Mechanical Engineering, Carnegie Mellon University 3 Machine Learning Department, Carnegie Mellon University |
| Pseudocode | No | The paper describes the steps of TPSR but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | 1The codes are available at: https://github.com/deep-symbolic-mathematics/TPSR |
| Open Datasets | Yes | We evaluate TPSR and various baseline methods on standard SR benchmark datasets from Penn Machine Learning Benchmark (PMLB) [43] studied in SRBench [42], as well as In-domain Synthetic Data generated based on [38, 18]. The benchmark datasets include 119 equations from Feynman Lectures on Physics database series2 [44], 14 symbolic regression problems from the ODE-Strogatz database3 [45], and 57 Black-box4 regression problems without known underlying equations. |
| Dataset Splits | No | The paper mentions '400 validation functions' for the In-domain Synthetic Data and uses SRBench datasets, but does not provide specific train/validation/test split percentages, sample counts, or detailed splitting methodology (e.g., random seed, stratified splitting) needed for reproducibility. It implies a 'test set' for evaluation and 'training points' for some analysis, but lacks explicit, comprehensive split details for all datasets. |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | Yes | For the E2E baseline, we use the settings reported in [18], including beam/sample size of C = 10 candidates, and the refinement of all the candidates K = 10. For our model, we use the width of tree search as kmax = 3, number of rollouts r = 3, and simulation beam size b = 1 as the default setting. |