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Machine learning and signal processing rely on universal function approximation. Multivariate polynomial models offer a natural way to express complex input-output relationships. The authors propose (MPO)$^2$, a method for multivariate polynomial optimization based on matrix product operators. This could improve function approximation and learning from limited data. If your model relies on polynomial approximations, you might want to take a closer look.
“arXiv:2607.15916v1 Announce Type: new Abstract: Central to machine learning and signal processing is the ability to perform universal function approximation and learn complex input-output relationships from limited numbers of observations. …”
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(MPO)$^2$
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Aug 5, 2026