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cs.LG2026
The Dynamics of Discoverability: How Trajectories and Priors Shape Equation Recovery
Matteo Gallo, Fabio Anselmi, Paolo Lazzari
How the dynamical regime of the observed system affects equation discovery has mainly been investigated through comparisons across systems. However, such comparisons vary both the…
cs.LG2026
PAC--Bayes Bounds on Quotient Parameter Spaces: Geometry-induced Implicit-Bias Priors
Nicola Aladrah, Fabio Anselmi
Overparameterized models often have continuous parameter symmetries, so different parameters define the same predictor. We show that PAC--Bayesian analysis should be performed on t…
cs.LG2026
Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
Nicola Aladrah, Emanuele Ballarin, Matteo Biagetti +3
Can we design a model such that its stochastic training favours a desired class of solutions without enforcing an explicit penalty? Under suitable conditions, the interplay between…