A fundamental question in theoretical machine learning is generalization. Over the past
decades, the PAC-Bayesian approach has been established as a flexible framework to …

While PAC-Bayes is now an established learning framework for light-tailed losses (\emph
{eg}, subgaussian or subexponential), its extension to the case of heavy-tailed losses …

We propose novel statistics which maximise the power of a two-sample test based on the
Maximum Mean Discrepancy (MMD), byadapting over the set of kernels used in defining it …

Minimising upper bounds on the population risk or the generalisation gap has been widely
used in structural risk minimisation (SRM)--this is in particular at the core of PAC-Bayesian …

We introduce a modified version of the excess risk, which can be used to obtain empirically
tighter, faster-rate PAC-Bayesian generalisation bounds. This modified excess risk …

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 …

Ensemble learning leverages multiple models (ie, weak learners) on a common machine
learning task to enhance prediction performance. Basic ensembling approaches average …

Generalisation in machine learning refers to the ability of a predictor learned on some
dataset to perform accurately on new, unseen data. Without generalisation, we might be …

Over-parameterised modern neural networks owe their success to two fundamental
properties: expressive power and generalisation capability. The former refers to the model's …