arXiv Machine Learning By Oussama Boussif, Mohammed Mahfoud, Younesse Kaddar, Moksh Jain, Sida Li, Damiano Fornasiere, Xiaoyin Chen, Yoshua Bengio, Esmeralda S. Whitammer

Bayesian Symbolic Regression with Entropic Reinforcement Learning

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arXiv:2608. 09617v1 Announce Type: new Abstract: Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of inputs.

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arXiv Machine Learning
Aug 31

Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests

The paper presents a probabilistic symbolic regression framework that models mathematical expressions as ensembles of symbolic trees, using a regularizing prior to control complexity and an Occam’s window-based posterior to capture uncertainty across plausible models. It provides theoretical guarantees on posterior concentration, including near‑parametric rates when an exact finite formula exists and oracle results under misspecification. Empirical results show the method outperforms state‑of‑the‑art competitors in predictive accuracy, symbolic complexity, and structural recovery on benchmark scientific equations and a materials discovery task.

By Somjit Roy, Pritam Dey, Bani K. Mallick, Debdeep Pati
arXiv Machine Learning
Sep 1

Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learning

arXiv:2506.17007v3 Announce Type: replace Abstract: A major bottleneck in scientific discovery consists of narrowing an exponentially large set of objects, such as proteins or molecules, to a small s...

By Marco Jiralerspong, Esther Derman, Danilo Vucetic, Esmeralda S. Whitammer, Bilun Sun, Tianyu Zhang, Pierre-Luc Bacon, Gauthier Gidel
arXiv AI
Jun 9

EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification

arXiv:2606. 07915v1 Announce Type: new Abstract: Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding is prone to error accumulation, which may lead to generating structurally incorrect expressions, especially in complex expression generation scenarios.

By Da Li, Xinxin Li, Xingyu Cui, Jin Xu, Juan Zhang, Junping Yin