Bingrun Chen — accepted AI-conference papers

2026

Neural–Evolutionary Symbolic Regression with Global Constraints: Constraint-Aware Decoding and Reward Shaping

Xiangdong Wu, Rongye Shi, Ziyu Wei, Bingrun Chen, Zhenbo Song, wenjun wu

ICML 2026poster

Symbolic regression discovers interpretable mathematical expressions from data and is central to scientific modeling. Recent neural approaches typically linearize expression trees into token sequences for sequential generation, but this representation weakens access to the underlying hierarchy and m…

Cited by 0 Source ↗Scholar ↗

2026

Progressive Subexpression Reuse in Symbolic Regression: Insights from RL-based Search and a Genetic Programming Realization

Xiangdong Wu, Wenjun Wu, Bingrun Chen, Junle Wang, Zhaoxin Fan, Haoyi Zhou +2

IJCAI 2026

Symbolic regression (SR) aims to recover compact and interpretable mathematical expressions from data. Genetic programming (GP) directly searches over symbolic structures, but its population dynamics can make it difficult to reliably preserve and accumulate useful subexpressions. In contrast, reinfo

Cited by 0 Scholar ↗