Benjamin Guedj

According to our database, Benjamin Guedj authored at least 63 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A note on regularised NTK dynamics with an application to PAC-Bayesian training.

Eugenio Clerico, Benjamin Guedj

Trans. Mach. Learn. Res., 2024

Learning via Surrogate PAC-Bayes.

Antoine Picard-Weibel, Roman Moscoviz, Benjamin Guedj

CoRR, 2024

Predicting Electricity Consumption with Random Walks on Gaussian Processes.

Chloé Hashimoto-Cullen, Benjamin Guedj

CoRR, 2024

Closed-form Filtering for Non-linear Systems.

Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi

CoRR, 2024

A PAC-Bayesian Link Between Generalisation and Flat Minima.

Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj

CoRR, 2024

Tighter Generalisation Bounds via Interpolation.

Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj

CoRR, 2024

Comparing Comparators in Generalization Bounds.

Fredrik Hellström, Benjamin Guedj

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Model validation using mutated training labels: An exploratory study.

Jie M. Zhang, Mark Harman, Benjamin Guedj, Earl T. Barr, John Shawe-Taylor

Neurocomputing, June, 2023

PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales.

Maxime Haddouche, Benjamin Guedj

Trans. Mach. Learn. Res., 2023

MMD Aggregated Two-Sample Test.

Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton

J. Mach. Learn. Res., 2023

Cluster-Specific Predictions with Multi-Task Gaussian Processes.

Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey

J. Mach. Learn. Res., 2023

Federated Learning with Nonvacuous Generalisation Bounds.

Pierre Jobic, Maxime Haddouche, Benjamin Guedj

CoRR, 2023

Generalization Bounds: Perspectives from Information Theory and PAC-Bayes.

Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky

CoRR, 2023

Wasserstein PAC-Bayes Learning: A Bridge Between Generalisation and Optimisation.

Maxime Haddouche, Benjamin Guedj

CoRR, 2023

Optimistic Dynamic Regret Bounds.

Maxime Haddouche, Benjamin Guedj, Olivier Wintenberger

CoRR, 2023

Learning via Wasserstein-Based High Probability Generalisation Bounds.

Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty.

Felix Biggs, Benjamin Guedj

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

MAGMA: inference and prediction using multi-task Gaussian processes with common mean.

Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey

Mach. Learn., 2022

PAC-Bayes with Unbounded Losses through Supermartingales.

Maxime Haddouche, Benjamin Guedj

CoRR, 2022

A PAC-Bayes bound for deterministic classifiers.

Eugenio Clerico, George Deligiannidis, Benjamin Guedj, Arnaud Doucet

CoRR, 2022

Reprint: a randomized extrapolation based on principal components for data augmentation.

Jiale Wei, Qiyuan Chen, Pai Peng, Benjamin Guedj, Le Li

CoRR, 2022

Depolarising Social Networks: Optimisation of Exposure to Adverse Opinions in the Presence of a Backfire Effect.

Antoine Vendeville, Benjamin Guedj, Shi Zhou

CoRR, 2022

On change of measure inequalities for f-divergences.

Antoine Picard-Weibel, Benjamin Guedj

CoRR, 2022

Controlling Confusion via Generalisation Bounds.

Reuben Adams, John Shawe-Taylor, Benjamin Guedj

CoRR, 2022

Efficient Aggregated Kernel Tests using Incomplete $U$-statistics.

Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

KSD Aggregated Goodness-of-fit Test.

Antonin Schrab, Benjamin Guedj, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Online PAC-Bayes Learning.

Maxime Haddouche, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Margins and Generalisation for Voting Classifiers.

Felix Biggs, Valentina Zantedeschi, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Measuring dissimilarity with diffeomorphism invariance.

Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi

Proceedings of the International Conference on Machine Learning, 2022

Non-Vacuous Generalisation Bounds for Shallow Neural Networks.

Felix Biggs, Benjamin Guedj

Proceedings of the International Conference on Machine Learning, 2022

Opening up Echo Chambers via Optimal Content Recommendation.

Antoine Vendeville, Anastasios Giovanidis, Effrosyni Papanastasiou, Benjamin Guedj

Proceedings of the Complex Networks and Their Applications XI, 2022

On PAC-Bayesian reconstruction guarantees for VAEs.

Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

On Margins and Derandomisation in PAC-Bayes.

Felix Biggs, Benjamin Guedj

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly.

Le Li, Benjamin Guedj

Entropy, 2021

PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses.

Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor

Entropy, 2021

Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds.

Benjamin Guedj, Louis Pujol

Entropy, 2021

Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks.

Felix Biggs, Benjamin Guedj

Entropy, 2021

Progress in Self-Certified Neural Networks.

María Pérez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor

CoRR, 2021

Learning PAC-Bayes Priors for Probabilistic Neural Networks.

María Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler

CoRR, 2021

Forecasting elections results via the voter model with stubborn nodes.

Antoine Vendeville, Benjamin Guedj, Shi Zhou

Appl. Netw. Sci., 2021

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound.

Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Towards Control of Opinion Diversity by Introducing Zealots into a Polarised Social Group.

Antoine Vendeville, Benjamin Guedj, Shi Zhou

Proceedings of the Complex Networks & Their Applications X - Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021, Madrid, Spain, November 30, 2021

Online k-means Clustering.

Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Kernel-Based Ensemble Learning in Python.

Benjamin Guedj, Bhargav Srinivasa Desikan

Inf., 2020

Attributing and Referencing (Research) Software: Best Practices and Outlook From Inria.

Pierre Alliez, Roberto Di Cosmo, Benjamin Guedj, Alain Girault, Mohand-Saïd Hacid, Arnaud Legrand, Nicolas P. Rougier

Comput. Sci. Eng., 2020

Upper and Lower Bounds on the Performance of Kernel PCA.

Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor

CoRR, 2020

A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings.

Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor

CoRR, 2020

An end-to-end data-driven optimisation framework for constrained trajectories.

Florent Dewez, Benjamin Guedj, Arthur Talpaert, Vincent Vandewalle

CoRR, 2020

MAGMA: Inference and Prediction with Multi-Task Gaussian Processes.

Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey

CoRR, 2020

How opinions crystallise: an analysis of polarisation in the voter model.

Antoine Vendeville, Benjamin Guedj, Shi Zhou

CoRR, 2020

From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning.

Florent Dewez, Benjamin Guedj, Vincent Vandewalle

CoRR, 2020

PAC-Bayesian Contrastive Unsupervised Representation Learning.

Kento Nozawa, Pascal Germain, Benjamin Guedj

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Non-linear Aggregation of Filters to Improve Image Denoising.

Benjamin Guedj, Juliette Rengot

Proceedings of the Intelligent Computing, 2020

PAC-Bayesian Bound for the Conditional Value at Risk.

Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Revisiting Clustering as Matrix Factorisation on the Stiefel Manifold.

Stéphane Chrétien, Benjamin Guedj

Proceedings of the Machine Learning, Optimization, and Data Science, 2020

2019

Perturbed Model Validation: A New Framework to Validate Model Relevance.

Jie M. Zhang, Earl T. Barr, Benjamin Guedj, Mark Harman, John Shawe-Taylor

CoRR, 2019

A Primer on PAC-Bayesian Learning.

Benjamin Guedj

CoRR, 2019

Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles.

John Klein, Mahmoud Albardan, Benjamin Guedj, Olivier Colot

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

PAC-Bayes Un-Expected Bernstein Inequality.

Zakaria Mhammedi, Peter Grünwald, Benjamin Guedj

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks.

Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Simpler PAC-Bayesian bounds for hostile data.

Pierre Alquier, Benjamin Guedj

Mach. Learn., 2018

2017

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation.

Benjamin Guedj, Bhargav Srinivasa Desikan

J. Mach. Learn. Res., 2017

2016

COBRA: A combined regression strategy.

Gérard Biau, Aurélie Fischer, Benjamin Guedj, James D. Malley

J. Multivar. Anal., 2016