34 citations · 94 across the 15 of their papers we have counts for
5 papers · 1 filter
Upper and Lower Bounds on the Performance of Kernel PCA
Maxime Haddouche, Benjamin Guedj, John Shawe-Taylor
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. More recently, kernel PCA (KPCA) has emerged as…
Tighter risk certificates for neural networks
María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor +1
This paper presents an empirical study regarding training probabilistic neural networks using training objectives derived from PAC-Bayes bounds. In the context of probabilistic neu…
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 unleashed: generalisation bounds with unbounded losses
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata +1
We present new PAC-Bayesian generalisation bounds for learning problems with unbounded loss functions. This extends the relevance and applicability of the PAC-Bayes learning framew…