{"type":"ScholarlyArticle","title":"Vertical Symbolic Regression via Deep Policy Gradient","authors":[{"name":"Nan Jiang","slug":"nan-jiang","given":"Nan","family":"Jiang","affiliation":"Department of Computer Science , Purdue University"},{"name":"Md Nasim","slug":"md-nasim","given":"Md","family":"Nasim","affiliation":"Department of Computer Science , Purdue University"},{"name":"Yexiang Xue","slug":"yexiang-xue","given":"Yexiang","family":"Xue","affiliation":"Department of Computer Science , Purdue University"}],"abstract":"Vertical Symbolic Regression (VSR) has recently been proposed to expedite the discovery of symbolic equations with many independent variables from experimental data. VSR reduces the search spaces following the vertical discovery path by building from reduced-form equations involving a subset of variables to all variables. While deep neural networks have shown promise in enhancing symbolic regression, directly integrating VSR with deep networks faces challenges such as gradient propagation and engineering complexities due to the tree representation of expressions. We propose Vertical Symbolic Regression using Deep Policy Gradient (VSR-DPG) and demonstrate that VSR-DPG can recover ground-truth equations involving multiple input variables, significantly beyond both deep reinforcement learning-based approaches and previous VSR variants. Our VSR-DPG models symbolic regression as a sequential decision-making process, in which equations are built from repeated applications of grammar rules. The integrated deep model is trained to maximize a policy gradient objective. 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.","doi":"10.24963/ijcai.2024/651","url":"https://ijcai.org/proceedings/2024/651","pdf":"https://ijcai.org/proceedings/2024/0651.pdf","markdown":"https://ijcai.org/proceedings/2024/651.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/651","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":651,"pages":{"first":5891,"last":5899},"keywords":["Multidisciplinary Topics and Applications: MTA: Physical sciences","Machine Learning: ML: Symbolic methods"],"videos":[],"references":[{"position":1,"raw":"[Abolafia et al., 2018] Daniel A. 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