Recent advancements in symbolic regression are reshaping its application across various fields, particularly in enhancing interpretability and robustness of predictive models. New frameworks, such as experience-driven goal-conditioned reinforcement learning, are steering the search process away from mere error minimization toward a more structured exploration of mathematical expressions, improving recovery rates for complex functions. Additionally, symbolic machine learning techniques are being employed to derive interpretable algebraic equations from chaotic time series data, bridging the gap between accuracy and transparency in forecasting. The integration of Bayesian methods allows for uncertainty quantification in discovered equations, addressing the limitations of traditional approaches. Furthermore, the application of mechanistic interpretability techniques to transformer-based models is unveiling the internal workings of symbolic regression, enhancing our understanding of how these models generate mathematical operators. Collectively, these developments are poised to solve commercial challenges in data-driven decision-making, where clarity and reliability of models are paramount.
Chaotic time series are notoriously difficult to forecast. Small uncertainties in initial conditions amplify rapidly, while strong nonlinearities and regime dependent variability constrain predictabil...
Symbolic Regression aims to automatically identify compact and interpretable mathematical expressions that model the functional relationship between input and output variables. Most existing search-ba...
Model-based approaches for (bio)process systems often suffer from incomplete knowledge of the underlying physical, chemical, or biological laws. Universal differential equations, which embed neural ne...
We introduce AI-Kolmogorov, a novel framework for Symbolic Density Estimation (SymDE). Symbolic regression (SR) has been effectively used to produce interpretable models in standard regression setting...
Following their success across many domains, transformers have also proven effective for symbolic regression (SR); however, the internal mechanisms underlying their generation of mathematical operator...
Symbolic Regression (SR) aims to discover interpretable equations from observational data, with the potential to reveal underlying principles behind natural phenomena. However, existing approaches oft...
Kolmogorov-Arnold networks (KANs) have arisen as a potential way to enhance the interpretability of machine learning. However, solutions learned by KANs are not necessarily interpretable, in the sense...
A fundamental but largely unaddressed obstacle in Symbolic regression (SR) is structural redundancy: every expression DAG with admits many distinct node-numbering schemes that all encode the same expr...
Symbolic regression aims to replace black-box predictors with concise analytical expressions that can be inspected and validated in scientific machine learning. Kolmogorov-Arnold Networks (KANs) are w...