COBRA: A combined regression strategy. - Gérard Biau; Aurélie Fischer; Benjamin Guedj; James D. Malley (J. Multivar. Anal., 2016)

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation. - Benjamin Guedj; Bhargav Srinivasa Desikan (J. Mach. Learn. Res., 2017)

Simpler PAC-Bayesian bounds for hostile data. - Pierre Alquier; Benjamin Guedj (Mach. Learn., 2018)

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. - Gaël Letarte; Pascal Germain; Benjamin Guedj; François Laviolette (NeurIPS, 2019)

PAC-Bayes Un-Expected Bernstein Inequality. - Zakaria Mhammedi; Peter Grünwald; Benjamin Guedj (NeurIPS, 2019)

Decentralized Learning with Budgeted Network Load Using Gaussian Copulas and Classifier Ensembles. - John Klein; Mahmoud Albardan; Benjamin Guedj; Olivier Colot (PKDD/ECML Workshops (1), 2019)

A Primer on PAC-Bayesian Learning. - Benjamin Guedj (CoRR, 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)

Revisiting Clustering as Matrix Factorisation on the Stiefel Manifold. - Stéphane Chrétien; Benjamin Guedj (LOD (1), 2020)

PAC-Bayesian Bound for the Conditional Value at Risk. - Zakaria Mhammedi; Benjamin Guedj; Robert C. Williamson (NeurIPS, 2020)

Non-linear Aggregation of Filters to Improve Image Denoising. - Benjamin Guedj; Juliette Rengot (SAI (2), 2020)

PAC-Bayesian Contrastive Unsupervised Representation Learning. - Kento Nozawa; Pascal Germain; Benjamin Guedj (UAI, 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)

How opinions crystallise: an analysis of polarisation in the voter model. - Antoine Vendeville; Benjamin Guedj; Shi Zhou (CoRR, 2020)

MAGMA: Inference and Prediction with Multi-Task Gaussian Processes. - Arthur Leroy; Pierre Latouche; Benjamin Guedj; Servane Gey (CoRR, 2020)

An end-to-end data-driven optimisation framework for constrained trajectories. - Florent Dewez; Benjamin Guedj; Arthur Talpaert; Vincent Vandewalle (CoRR, 2020)

A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings. - Théophile Cantelobre; Benjamin Guedj; María Pérez-Ortiz 001; John Shawe-Taylor (CoRR, 2020)

Upper and Lower Bounds on the Performance of Kernel PCA. - Maxime Haddouche; Benjamin Guedj; Omar Rivasplata; John Shawe-Taylor (CoRR, 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)

Kernel-Based Ensemble Learning in Python. - Benjamin Guedj; Bhargav Srinivasa Desikan (Inf., 2020)

Online k-means Clustering. - Vincent Cohen-Addad; Benjamin Guedj; Varun Kanade; Guy Rom (AISTATS, 2021)

Towards Control of Opinion Diversity by Introducing Zealots into a Polarised Social Group. - Antoine Vendeville; Benjamin Guedj; Shi Zhou (COMPLEX NETWORKS, 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 (NeurIPS, 2021)

Forecasting elections results via the voter model with stubborn nodes. - Antoine Vendeville; Benjamin Guedj; Shi Zhou (Appl. Netw. Sci., 2021)

Learning PAC-Bayes Priors for Probabilistic Neural Networks. - María Pérez-Ortiz 001; Omar Rivasplata; Benjamin Guedj; Matthew Gleeson; Jingyu Zhang; John Shawe-Taylor; Miroslaw Bober; Josef Kittler (CoRR, 2021)

Progress in Self-Certified Neural Networks. - María Pérez-Ortiz 001; Omar Rivasplata; Emilio Parrado-Hernández; Benjamin Guedj; John Shawe-Taylor (CoRR, 2021)

Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks. - Felix Biggs; Benjamin Guedj (Entropy, 2021)

Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds. - Benjamin Guedj; Louis Pujol (Entropy, 2021)

PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. - Maxime Haddouche; Benjamin Guedj; Omar Rivasplata; John Shawe-Taylor (Entropy, 2021)

Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly. - Le Li; Benjamin Guedj (Entropy, 2021)

On Margins and Derandomisation in PAC-Bayes. - Felix Biggs; Benjamin Guedj (AISTATS, 2022)

On PAC-Bayesian reconstruction guarantees for VAEs. - Badr-Eddine Chérief-Abdellatif; Yuyang Shi 002; Arnaud Doucet; Benjamin Guedj (AISTATS, 2022)

Opening up Echo Chambers via Optimal Content Recommendation. - Antoine Vendeville; Anastasios Giovanidis; Effrosyni Papanastasiou; Benjamin Guedj (COMPLEX NETWORKS (1), 2022)

Non-Vacuous Generalisation Bounds for Shallow Neural Networks. - Felix Biggs; Benjamin Guedj (ICML, 2022)

Measuring dissimilarity with diffeomorphism invariance. - Théophile Cantelobre; Carlo Ciliberto; Benjamin Guedj; Alessandro Rudi (ICML, 2022)

On Margins and Generalisation for Voting Classifiers. - Felix Biggs; Valentina Zantedeschi; Benjamin Guedj (NeurIPS, 2022)

Online PAC-Bayes Learning. - Maxime Haddouche; Benjamin Guedj (NeurIPS, 2022)

KSD Aggregated Goodness-of-fit Test. - Antonin Schrab; Benjamin Guedj; Arthur Gretton (NeurIPS, 2022)

Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. - Antonin Schrab; Ilmun Kim; Benjamin Guedj; Arthur Gretton (NeurIPS, 2022)

Controlling Confusion via Generalisation Bounds. - Reuben Adams; John Shawe-Taylor; Benjamin Guedj (CoRR, 2022)

On change of measure inequalities for f-divergences. - Antoine Picard-Weibel; Benjamin Guedj (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)

Reprint: a randomized extrapolation based on principal components for data augmentation. - Jiale Wei; Qiyuan Chen 003; Pai Peng; Benjamin Guedj; Le Li (CoRR, 2022)

A PAC-Bayes bound for deterministic classifiers. - Eugenio Clerico; George Deligiannidis; Benjamin Guedj; Arnaud Doucet (CoRR, 2022)

PAC-Bayes with Unbounded Losses through Supermartingales. - Maxime Haddouche; Benjamin Guedj (CoRR, 2022)

MAGMA: inference and prediction using multi-task Gaussian processes with common mean. - Arthur Leroy; Pierre Latouche; Benjamin Guedj; Servane Gey (Mach. Learn., 2022)

Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty. - Felix Biggs; Benjamin Guedj (AISTATS, 2023)

Learning via Wasserstein-Based High Probability Generalisation Bounds. - Paul Viallard; Maxime Haddouche; Umut Simsekli; Benjamin Guedj (NeurIPS, 2023)

Optimistic Dynamic Regret Bounds. - Maxime Haddouche; Benjamin Guedj; Olivier Wintenberger (CoRR, 2023)

Wasserstein PAC-Bayes Learning: A Bridge Between Generalisation and Optimisation. - Maxime Haddouche; Benjamin Guedj (CoRR, 2023)

Federated Learning with Nonvacuous Generalisation Bounds. - Pierre Jobic; Maxime Haddouche; Benjamin Guedj (CoRR, 2023)

Cluster-Specific Predictions with Multi-Task Gaussian Processes. - Arthur Leroy; Pierre Latouche; Benjamin Guedj; Servane Gey (J. 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)

PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales. - Maxime Haddouche; Benjamin Guedj (Trans. Mach. Learn. Res., 2023)

Model validation using mutated training labels: An exploratory study. - Jie M. Zhang; Mark Harman; Benjamin Guedj; Earl T. Barr; John Shawe-Taylor (Neurocomputing, 2023)

Comparing Comparators in Generalization Bounds. - Fredrik Hellström; Benjamin Guedj (AISTATS, 2024)

Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound. - Reuben Adams; John Shawe-Taylor; Benjamin Guedj (NeurIPS, 2024)

Learning via Surrogate PAC-Bayes. - Antoine Picard-Weibel; Roman Moscoviz; Benjamin Guedj (NeurIPS, 2024)

Tighter Generalisation Bounds via Interpolation. - Paul Viallard; Maxime Haddouche; Umut Simsekli; Benjamin Guedj (CoRR, 2024)

Closed-form Filtering for Non-linear Systems. - Théophile Cantelobre; Carlo Ciliberto; Benjamin Guedj; Alessandro Rudi (CoRR, 2024)

Predicting Electricity Consumption with Random Walks on Gaussian Processes. - Chloé Hashimoto-Cullen; Benjamin Guedj (CoRR, 2024)

AI for ERW Detection in Clearance Operations - The State of Research. - Björn Kischelewski; Gregory Cathcart; David Wahl; Benjamin Guedj (CoRR, 2024)

A note on regularised NTK dynamics with an application to PAC-Bayesian training. - Eugenio Clerico; Benjamin Guedj (Trans. Mach. Learn. Res., 2024)

Generalisation under gradient descent via deterministic PAC-Bayes. - Eugenio Clerico; Tyler Farghly; George Deligiannidis; Benjamin Guedj; Arnaud Doucet (ALT, 2025)

A PAC-Bayesian Link Between Generalisation and Flat Minima. - Maxime Haddouche; Paul Viallard; Umut Simsekli; Benjamin Guedj (ALT, 2025)

Generalization Bounds: Perspectives from Information Theory and PAC-Bayes. - Fredrik Hellström; Giuseppe Durisi; Benjamin Guedj; Maxim Raginsky (Found. Trends Mach. Learn., 2025)

How good is PAC-Bayes at explaining generalisation? - Antoine Picard-Weibel; Eugenio Clerico; Roman Moscoviz; Benjamin Guedj (CoRR, 2025)

RestoreAI - Pattern-based Risk Estimation Of Remaining Explosives. - Björn Kischelewski; Benjamin Guedj; David Wahl (CoRR, 2025)