Author

Nicola Aladrah

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#machine learning Preprint Jan 2026

Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective

A framework for inverse-designing such biases by constructing novel parametrizations and their associated symmetries is developed and it is shown how holomorphic functions make this construction and calculation simple and explicit.

Nicola Aladrah, Emanuele Ballarin, Matteo Biagetti et al. · 3 citations

Jul 2026

PAC-Bayes Bounds on Quotient Parameter Spaces: Geometry-induced Implicit-Bias Priors

It is shown that PAC--Bayesian analysis should be performed on the quotient predictor space: pushing a prior and posterior to the quotient preserves the empirical and population Gibbs risks while removing the nonnegative KL contribution caused solely by how the two distributions differ among parameterizations of the sa...

Nicola Aladrah, Fabio Anselmi · arXiv.org · 0 citations