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cs.LG2026
A differentiability framework for zigzag persistent homology via linear interpolation
Enrico Maria Ferrari, Clemens Bannwart, Matteo Biagetti
Persistent homology can be differentiated and incorporated into learning pipelines, but no analogous framework exists for zigzag persistence, which is needed when the underlying to…
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…