no code implementations • 26 Apr 2024 • Benjamin Dupuis, Paul Viallard, George Deligiannidis, Umut Simsekli
We propose data-dependent uniform generalization bounds by approaching the problem from a PAC-Bayesian perspective.
no code implementations • 12 Feb 2024 • Benjamin Dupuis, Umut Şimşekli
Understanding the generalization properties of heavy-tailed stochastic optimization algorithms has attracted increasing attention over the past years.
no code implementations • 1 Dec 2023 • Benjamin Dupuis, Paul Viallard
This has been successfully applied to generalization theory by exploiting the fractal properties of those dynamics.
1 code implementation • 6 Feb 2023 • Benjamin Dupuis, George Deligiannidis, Umut Şimşekli
To achieve this goal, we build up on a classical covering argument in learning theory and introduce a data-dependent fractal dimension.
1 code implementation • NeurIPS 2021 • Benjamin Dupuis, Arthur Jacot
We study the Solid Isotropic Material Penalisation (SIMP) method with a density field generated by a fully-connected neural network, taking the coordinates as inputs.