49 citations · 412 across the 50 of their papers we have counts for
17 papers · 1 filter
Learning via Wasserstein-Based High Probability Generalisation Bounds
Paul Viallard, Maxime Haddouche, Umut Şimşekli +1
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-Baye…
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
Maxime Haddouche, Benjamin Guedj
PAC-Bayes learning is an established framework to both assess the generalisation ability of learning algorithms, and design new learning algorithm by exploiting generalisation boun…
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales
Maxime Haddouche, Benjamin Guedj
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 rema…
Generalisation under gradient descent via deterministic PAC-Bayes
Eugenio Clerico, Tyler Farghly, George Deligiannidis +2
We establish disintegrated PAC-Bayesian generalisation bounds for models trained with gradient descent methods or continuous gradient flows. Contrary to standard practice in the PA…
Measuring dissimilarity with diffeomorphism invariance
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj +1
Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms. We introduce DID, a pairwise dissimilarity measure applicable to a wide range of…
Change of measure through the Legendre transform
Antoine Picard-Weibel, Benjamin Guedj
PAC-Bayes generalisation bounds are derived via change-of-measure inequalities that transfer concentration properties from a reference measure to all posterior measures. The specif…
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
Reuben Adams, John Shawe-Taylor, Benjamin Guedj
Current PAC-Bayes generalisation bounds are restricted to scalar metrics of performance, such as the loss or error rate. However, one ideally wants more information-rich certificat…
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…
Non-linear aggregation of filters to improve image denoising
Benjamin Guedj, Juliette Rengot
We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity ba…
A Primer on PAC-Bayesian Learning
Benjamin Guedj
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providi…
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
Benjamin Guedj, Le Li
When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. Principal curves act as a nonl…
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
John Klein, Mahmoud Albardan, Benjamin Guedj +1
We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models.…
Simpler PAC-Bayesian Bounds for Hostile Data
Pierre Alquier, Benjamin Guedj
PAC-Bayesian learning bounds are of the utmost interest to the learning community. Their role is to connect the generalization ability of an aggregation distribution to its emp…
Stability revisited: new generalisation bounds for the Leave-one-Out
Alain Celisse, Benjamin Guedj
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. T…
A Quasi-Bayesian Perspective to Online Clustering
Le Li, Benjamin Guedj, Sébastien Loustau
When faced with high frequency streams of data, clustering raises theoretical and algorithmic pitfalls. We introduce a new and adaptive online clustering algorithm relying on a qua…
An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization
Pierre Alquier, Benjamin Guedj
The aim of this paper is to provide some theoretical understanding of quasi-Bayesian aggregation methods non-negative matrix factorization. We derive an oracle inequality for an ag…
PAC-Bayesian High Dimensional Bipartite Ranking
Benjamin Guedj, Sylvain Robbiano
This paper is devoted to the bipartite ranking problem, a classical statistical learning task, in a high dimensional setting. We propose a scoring and ranking strategy based on the…