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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2602. 23561v2 Announce Type: replace-cross Abstract: Symbolic regression (SR) has gained recent traction in AI-driven scientific discovery for learning closed-form physical laws.
arXiv:2606. 06724v1 Announce Type: new Abstract: Representative data is fundamental in machine learning, as limited data hinders generalisation.
arXiv:2606. 00680v1 Announce Type: new Abstract: Offline reinforcement learning (RL) aims to optimize policies from pre-collected datasets.
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.
arXiv:2511. 04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data.
arXiv:2602. 10576v2 Announce Type: replace-cross Abstract: Symbolic regression aims to distill mathematical equations from observational data.