49 citations · 412 across the 50 of their papers we have counts for
4 papers · 1 filter
Progress in Self-Certified Neural Networks
Maria Perez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernandez +2
A learning method is self-certified if it uses all available data to simultaneously learn a predictor and certify its quality with a tight statistical certificate that is valid on…
Learning PAC-Bayes Priors for Probabilistic Neural Networks
Maria Perez-Ortiz, Omar Rivasplata, Benjamin Guedj +5
Recent works have investigated deep learning models trained by optimising PAC-Bayes bounds, with priors that are learnt on subsets of the data. This combination has been shown to l…
On Margins and Derandomisation in PAC-Bayes
Felix Biggs, Benjamin Guedj
We give a general recipe for derandomising PAC-Bayesian bounds using margins, with the critical ingredient being that our randomised predictions concentrate around some value. The…
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
Valentina Zantedeschi, Paul Viallard, Emilie Morvant +4
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary dist…