Benjamin Guedj

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 92

Top Topics

Generalization Bounds

Top Venues

Publications

Eugenio Clerico, Benjamin Guedj
A note on regularised NTK dynamics with an application to PAC-Bayesian training. Trans. Mach. Learn. Res. 2024 (2024)
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Closed-form Filtering for Non-linear Systems. CoRR (2024)
Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj
A PAC-Bayesian Link Between Generalisation and Flat Minima. CoRR (2024)
Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj
Tighter Generalisation Bounds via Interpolation. CoRR (2024)
Fredrik Hellström, Benjamin Guedj
Comparing Comparators in Generalization Bounds. AISTATS (2024)
Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj
Learning via Wasserstein-Based High Probability Generalisation Bounds. NeurIPS (2023)
Pierre Jobic, Maxime Haddouche, Benjamin Guedj
Federated Learning with Nonvacuous Generalisation Bounds. CoRR (2023)
Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey
Cluster-Specific Predictions with Multi-Task Gaussian Processes. J. Mach. Learn. Res. 24 (2023)
Eugenio Clerico, Benjamin Guedj
A note on regularised NTK dynamics with an application to PAC-Bayesian training. CoRR (2023)
Paul Viallard, Maxime Haddouche, Umut Simsekli, Benjamin Guedj
Learning via Wasserstein-Based High Probability Generalisation Bounds. CoRR (2023)
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes. CoRR (2023)
Maxime Haddouche, Benjamin Guedj, Olivier Wintenberger
Optimistic Dynamic Regret Bounds. CoRR (2023)
Maxime Haddouche, Benjamin Guedj
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales. Trans. Mach. Learn. Res. 2023 (2023)
Fredrik Hellström, Benjamin Guedj
Comparing Comparators in Generalization Bounds. CoRR (2023)
Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton
MMD Aggregated Two-Sample Test. J. Mach. Learn. Res. 24 (2023)
Felix Biggs, Benjamin Guedj
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty. AISTATS (2023)
Maxime Haddouche, Benjamin Guedj
Wasserstein PAC-Bayes Learning: A Bridge Between Generalisation and Optimisation. CoRR (2023)
Jie M. Zhang, Mark Harman, Benjamin Guedj, Earl T. Barr, John Shawe-Taylor
Model validation using mutated training labels: An exploratory study. Neurocomputing 539 (2023)
Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton
Efficient Aggregated Kernel Tests using Incomplete U-statistics. CoRR (2022)
Maxime Haddouche, Benjamin Guedj
Online PAC-Bayes Learning. NeurIPS (2022)
Maxime Haddouche, Benjamin Guedj
PAC-Bayes with Unbounded Losses through Supermartingales. CoRR (2022)
Felix Biggs, Benjamin Guedj
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty. CoRR (2022)
Felix Biggs, Benjamin Guedj
On Margins and Derandomisation in PAC-Bayes. AISTATS (2022)
Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
On Margins and Generalisation for Voting Classifiers. CoRR (2022)
Antoine Vendeville, Anastasios Giovanidis, Effrosyni Papanastasiou, Benjamin Guedj
Opening up Echo Chambers via Optimal Content Recommendation. COMPLEX NETWORKS (1) (2022)
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Measuring dissimilarity with diffeomorphism invariance. CoRR (2022)
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Measuring dissimilarity with diffeomorphism invariance. ICML (2022)
Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj
On PAC-Bayesian reconstruction guarantees for VAEs. AISTATS (2022)
Maxime Haddouche, Benjamin Guedj
Online PAC-Bayes Learning. CoRR (2022)
Antoine Picard-Weibel, Benjamin Guedj
On change of measure inequalities for f-divergences. CoRR (2022)
Eugenio Clerico, George Deligiannidis, Benjamin Guedj, Arnaud Doucet
A PAC-Bayes bound for deterministic classifiers. CoRR (2022)
Felix Biggs, Benjamin Guedj
Non-Vacuous Generalisation Bounds for Shallow Neural Networks. CoRR (2022)
Antonin Schrab, Benjamin Guedj, Arthur Gretton
KSD Aggregated Goodness-of-fit Test. NeurIPS (2022)
Antoine Vendeville, Anastasios Giovanidis, Effrosyni Papanastasiou, Benjamin Guedj
Opening up echo chambers via optimal content recommendation. CoRR (2022)
Reuben Adams, John Shawe-Taylor, Benjamin Guedj
Controlling Confusion via Generalisation Bounds. CoRR (2022)
Antoine Vendeville, Benjamin Guedj, Shi Zhou
Depolarising Social Networks: Optimisation of Exposure to Adverse Opinions in the Presence of a Backfire Effect. CoRR (2022)
Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey
MAGMA: inference and prediction using multi-task Gaussian processes with common mean. Mach. Learn. 111 (5) (2022)
Felix Biggs, Valentina Zantedeschi, Benjamin Guedj
On Margins and Generalisation for Voting Classifiers. NeurIPS (2022)
Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. NeurIPS (2022)
Antonin Schrab, Benjamin Guedj, Arthur Gretton
KSD Aggregated Goodness-of-fit Test. CoRR (2022)
Felix Biggs, Benjamin Guedj
Non-Vacuous Generalisation Bounds for Shallow Neural Networks. ICML (2022)
Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj
On PAC-Bayesian reconstruction guarantees for VAEs. CoRR (2022)
Jiale Wei, Qiyuan Chen, Pai Peng, Benjamin Guedj, Le Li
Reprint: a randomized extrapolation based on principal components for data augmentation. CoRR (2022)
Le Li, Benjamin Guedj
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly. Entropy 23 (11) (2021)
Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. NeurIPS (2021)
Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton
MMD Aggregated Two-Sample Test. CoRR (2021)
Antoine Vendeville, Benjamin Guedj, Shi Zhou
Forecasting elections results via the voter model with stubborn nodes. Appl. Netw. Sci. 6 (1) (2021)
Felix Biggs, Benjamin Guedj
Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks. Entropy 23 (10) (2021)
Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. CoRR (2021)
Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom
Online k-means Clustering. AISTATS (2021)
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. Entropy 23 (10) (2021)
Benjamin Guedj, Louis Pujol
Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds. Entropy 23 (11) (2021)
Antoine Vendeville, Benjamin Guedj, Shi Zhou
Towards Control of Opinion Diversity by Introducing Zealots into a Polarised Social Group. COMPLEX NETWORKS (2021)
María Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler
Learning PAC-Bayes Priors for Probabilistic Neural Networks. CoRR (2021)
Felix Biggs, Benjamin Guedj
On Margins and Derandomisation in PAC-Bayes. CoRR (2021)
María Pérez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor
Progress in Self-Certified Neural Networks. CoRR (2021)
Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey
Cluster-Specific Predictions with Multi-Task Gaussian Processes. CoRR (2020)
Antoine Vendeville, Benjamin Guedj, Shi Zhou
How opinions crystallise: an analysis of polarisation in the voter model. CoRR (2020)
Arthur Leroy, Pierre Latouche, Benjamin Guedj, Servane Gey
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes. CoRR (2020)
Pierre Alliez, Roberto Di Cosmo, Benjamin Guedj, Alain Girault, Mohand-Saïd Hacid, Arnaud Legrand, Nicolas P. Rougier
Attributing and Referencing (Research) Software: Best Practices and Outlook From Inria. Comput. Sci. Eng. 22 (1) (2020)
Kento Nozawa, Pascal Germain, Benjamin Guedj
PAC-Bayesian Contrastive Unsupervised Representation Learning. UAI (2020)
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
Upper and Lower Bounds on the Performance of Kernel PCA. CoRR (2020)
Benjamin Guedj, Juliette Rengot
Non-linear Aggregation of Filters to Improve Image Denoising. SAI (2) (2020)
Florent Dewez, Benjamin Guedj, Vincent Vandewalle
From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning. CoRR (2020)
Benjamin Guedj, Bhargav Srinivasa Desikan
Kernel-Based Ensemble Learning in Python. Inf. 11 (2) (2020)
Stéphane Chrétien, Benjamin Guedj
Revisiting Clustering as Matrix Factorisation on the Stiefel Manifold. LOD (1) (2020)
Antoine Vendeville, Benjamin Guedj, Shi Zhou
Forecasting elections results via the voter model with stubborn nodes. CoRR (2020)
Felix Biggs, Benjamin Guedj
Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks. CoRR (2020)
Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson
PAC-Bayesian Bound for the Conditional Value at Risk. CoRR (2020)
Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson
PAC-Bayesian Bound for the Conditional Value at Risk. NeurIPS (2020)
Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings. CoRR (2020)
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
PAC-Bayes unleashed: generalisation bounds with unbounded losses. CoRR (2020)
Florent Dewez, Benjamin Guedj, Arthur Talpaert, Vincent Vandewalle
An end-to-end data-driven optimisation framework for constrained trajectories. CoRR (2020)
Kento Nozawa, Pascal Germain, Benjamin Guedj
PAC-Bayesian Contrastive Unsupervised Representation Learning. CoRR (2019)
Benjamin Guedj, Bhargav Srinivasa Desikan
Kernel-Based Ensemble Learning in Python. CoRR (2019)
Zakaria Mhammedi, Peter D. Grünwald, Benjamin Guedj
PAC-Bayes Un-Expected Bernstein Inequality. CoRR (2019)
Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom
Online k-means Clustering. CoRR (2019)
Stéphane Chrétien, Benjamin Guedj
Revisiting clustering as matrix factorisation on the Stiefel manifold. CoRR (2019)
Benjamin Guedj, Juliette Rengot
Non-linear aggregation of filters to improve image denoising. CoRR (2019)
Pierre Alliez, Roberto Di Cosmo, Benjamin Guedj, Alain Girault, Mohand-Said Hacid, Arnaud Legrand, Nicolas P. Rougier
Attributing and Referencing (Research) Software: Best Practices and Outlook from Inria. CoRR (2019)
Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. CoRR (2019)
Jie M. Zhang, Earl T. Barr, Benjamin Guedj, Mark Harman, John Shawe-Taylor
Perturbed Model Validation: A New Framework to Validate Model Relevance. CoRR (2019)
Benjamin Guedj, Louis Pujol
Still no free lunches: the price to pay for tighter PAC-Bayes bounds. CoRR (2019)
Zakaria Mhammedi, Peter Grünwald, Benjamin Guedj
PAC-Bayes Un-Expected Bernstein Inequality. NeurIPS (2019)
Benjamin Guedj
A Primer on PAC-Bayesian Learning. CoRR (2019)
Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. NeurIPS (2019)
John Klein, Mahmoud Albardan, Benjamin Guedj, Olivier Colot
Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles. PKDD/ECML Workshops (1) (2019)
John Klein, Mahmoud Albardan, Benjamin Guedj, Olivier Colot
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles. CoRR (2018)
Benjamin Guedj, Le Li
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly. CoRR (2018)
Pierre Alquier, Benjamin Guedj
Simpler PAC-Bayesian bounds for hostile data. Mach. Learn. 107 (5) (2018)
Benjamin Guedj, Bhargav Srinivasa Desikan
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation. J. Mach. Learn. Res. 18 (2017)
Gérard Biau, Aurélie Fischer, Benjamin Guedj, James D. Malley
COBRA: A combined regression strategy. J. Multivar. Anal. 146 (2016)