7 citations · 10 across the 3 of their papers we have counts for
7 papers
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 the Role of Optimization in Double Descent: A Least Squares Study
Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata +2
Empirically it has been observed that the performance of deep neural networks steadily improves as we increase model size, contradicting the classical view on overfitting and gener…
A note on a confidence bound of Kuzborskij and Szepesvári
Omar Rivasplata
In an interesting recent work, Kuzborskij and Szepesvári derived a confidence bound for functions of independent random variables, which is based on an inequality that relates conc…
PAC-Bayes Analysis Beyond the Usual Bounds
Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari +1
We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a…
Logarithmic Pruning is All You Need
Laurent Orseau, Marcus Hutter, Omar Rivasplata
The Lottery Ticket Hypothesis is a conjecture that every large neural network contains a subnetwork that, when trained in isolation, achieves comparable performance to the large ne…
PAC-Bayes with Backprop
Omar Rivasplata, Vikram M Tankasali, Csaba Szepesvari
We explore the family of methods "PAC-Bayes with Backprop" (PBB) to train probabilistic neural networks by minimizing PAC-Bayes bounds. We present two training objectives, one deri…