Full Bayesian inference with hazard mixture models. - Julyan Arbel; Antonio Lijoi; Bernardo Nipoti (Comput. Stat. Data Anal., 2016)

A moment-matching Ferguson & Klass algorithm. - Julyan Arbel; Igor Prünster (Stat. Comput., 2017)

Bayesian neural networks increasingly sparsify their units with depth. - Mariia Vladimirova; Julyan Arbel; Pablo Mesejo (CoRR, 2018)

Understanding Priors in Bayesian Neural Networks at the Unit Level. - Mariia Vladimirova; Jakob Verbeek; Pablo Mesejo; Julyan Arbel (ICML, 2019)

Quantitative Mri Characterization of Brain Abnormalities in DE NOVO Parkinsonian Patients. - Verónica Muñoz Ramírez; Florence Forbes; Julyan Arbel; Alexis Arnaud; Michel Dojat (ISBI, 2019)

Dependence properties and Bayesian inference for asymmetric multivariate copulas. - Julyan Arbel; Marta Crispino; Stéphane Girard (J. Multivar. Anal., 2019)

Approximate Bayesian Computation Via the Energy Statistic. - Hien Duy Nguyen; Julyan Arbel; Hongliang Lü; Florence Forbes (IEEE Access, 2020)

Bayesian nonparametric priors for hidden Markov random fields. - Hongliang Lü; Julyan Arbel; Florence Forbes (Stat. Comput., 2020)

Bayesian neural network unit priors and generalized Weibull-tail property. - Mariia Vladimirova; Julyan Arbel; Stéphane Girard (ACML, 2021)

LDpred2: better, faster, stronger. - Florian Privé; Julyan Arbel; Bjarni J. Vilhjálmsson (Bioinform., 2021)

Dirichlet process mixtures under affine transformations of the data. - Julyan Arbel; Riccardo Corradin; Bernardo Nipoti (Comput. Stat., 2021)

Bayesian Inverse Regression for Vascular Magnetic Resonance Fingerprinting. - Fabien Boux; Florence Forbes; Julyan Arbel; Benjamin Lemasson; Emmanuel Luc Barbier (IEEE Trans. Medical Imaging, 2021)

Imposing Gaussian Pre-Activations in a Neural Network. - Pierre Wolinski; Julyan Arbel (CoRR, 2022)

Cold Posteriors through PAC-Bayes. - Konstantinos Pitas; Julyan Arbel (CoRR, 2022)

Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors. - Florence Forbes; Hien Duy Nguyen; TrungTin Nguyen; Julyan Arbel (Stat. Comput., 2022)

The Fine Print on Tempered Posteriors. - Konstantinos Pitas; Julyan Arbel (ACML, 2023)

TinyMLOps for real-time ultra-low power MCUs applied to frame-based event classification. - Minh Tri Le; Julyan Arbel (EuroMLSys@EuroSys, 2023)

Regularization for Hybrid N-Bit Weight Quantization of Neural Networks on Ultra-Low Power Microcontrollers. - Minh Tri Le; Etienne de Foras; Julyan Arbel (ICANN (5), 2023)

Hybrids of Constraint-based and Noise-based Algorithms for Causal Discovery from Time Series. - Charles K. Assaad; Daria Bystrova; Julyan Arbel; Emilie Devijver; Éric Gaussier; Wilfried Thuiller (CoRR, 2023)

A Primer on Bayesian Neural Networks: Review and Debates. - Julyan Arbel; Konstantinos Pitas; Mariia Vladimirova; Vincent Fortuin (CoRR, 2023)

Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data. - Konstantinos Pitas; Julyan Arbel (CoRR, 2023)

Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review. - Minh Tri Le; Pierre Wolinski; Julyan Arbel (CoRR, 2023)

Reparameterization of extreme value framework for improved Bayesian workflow. - Théo Moins; Julyan Arbel; Stéphane Girard; Anne Dutfoy (Comput. Stat. Data Anal., 2023)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits. - Julien Zhou; Pierre Gaillard; Thibaud Rahier; Houssam Zenati; Julyan Arbel (NeurIPS, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Covariance-Adaptive Least-Squares Algorithm for Stochastic Combinatorial Semi-Bandits. - Julien Zhou; Pierre Gaillard; Thibaud Rahier; Houssam Zenati; Julyan Arbel (CoRR, 2024)

Just rotate it! Uncertainty estimation in closed-source models via multiple queries. - Konstantinos Pitas; Julyan Arbel (CoRR, 2024)

Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms. - Daria Bystrova; Charles K. Assaad; Julyan Arbel; Emilie Devijver; Éric Gaussier; Wilfried Thuiller (Trans. Mach. Learn. Res., 2024)

Shrinkage for extreme partial least-squares. - Julyan Arbel; Stéphane Girard; Hadrien Lorenzo (Stat. Comput., 2024)

U-ensembles: Improved diversity in the small data regime using unlabeled data. - Konstantinos Pitas; Hani Anouar Bourrous; Julyan Arbel (AABI, 2025)

Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit. - Julien Zhou; Pierre Gaillard; Thibaud Rahier; Julyan Arbel (ALT, 2025)

Gaussian Pre-Activations in Neural Networks: Myth or Reality? - Pierre Wolinski; Julyan Arbel (Trans. Mach. Learn. Res., 2025)

Natural Variational Annealing for Multimodal Optimization. - Tâm Le Minh; Julyan Arbel; Thomas Möllenhoff; Mohammad Emtiyaz Khan; Florence Forbes (CoRR, 2025)