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Found 9 papers, 0 papers with code
A PAC-Bayesian Link Between Generalisation and Flat Minima
Modern machine learning usually involves predictors in the overparametrised setting (number of trained parameters greater than dataset size), and their training yield not only good performances on training data, but also good generalisation capacity.
Tighter Generalisation Bounds via Interpolation
We also instantiate our bounds as training objectives, yielding non-trivial guarantees and practical performances.
Federated Learning with Nonvacuous Generalisation Bounds
We introduce a novel strategy to train randomised predictors in federated learning, where each node of the network aims at preserving its privacy by releasing a local predictor but keeping secret its training dataset with respect to the other nodes.
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
PAC-Bayes learning is an established framework to both assess the generalisation ability of learning algorithms, and design new learning algorithm by exploiting generalisation bounds as training objectives.
Optimistically Tempered Online Learning
Optimistic Online Learning algorithms have been developed to exploit expert advices, assumed optimistically to be always useful.
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales
While PAC-Bayes is now an established learning framework for light-tailed losses (\emph{e. g.}, subgaussian or subexponential), its extension to the case of heavy-tailed losses remains largely uncharted and has attracted a growing interest in recent years.
Online PAC-Bayes Learning
Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at once, prior to inference or prediction.
Upper and Lower Bounds on the Performance of Kernel PCA
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades.
PAC-Bayes unleashed: generalisation bounds with unbounded losses
We present new PAC-Bayesian generalisation bounds for learning problems with unbounded loss functions.
