Benjamin Guedj

Stability revisited: new generalisation bounds for the Leave-one-Out

2016 · arXiv (Cornell University)

The present paper provides a new generic strategy leading to non-asymptotic theoretical guarantees on the Leave-one-Out procedure applied to a broad class of learning algorithms. This strategy relies on two main ingredients: the new notion …

Cluster-Specific Predictions with Multi-Task Gaussian Processes

2020 · arXiv (Cornell University)

A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a …

Generalisation under gradient descent via deterministic PAC-Bayes

2022 · arXiv (Cornell University)

We establish disintegrated PAC-Bayesian generalisation bounds for models trained with gradient descent methods or continuous gradient flows. Contrary to standard practice in the PAC-Bayesian setting, our result applies to optimisation algorithms that are deterministic, without …