Assessing dynamic functional connectivity in heterogeneous samples. - B. C. L. Lehmann; Simon R. White; Richard N. Henson; Cam-CAN Group; Linda Geerligs (NeuroImage, 2017)

Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models. - B. C. L. Lehmann; Richard N. A. Henson; Linda Geerligs; Cam-CAN Group; Simon R. White (NeuroImage, 2021)

Neural score matching for high-dimensional causal inference. - Oscar Clivio; Fabian Falck; Brieuc Lehmann; George Deligiannidis; Chris C. Holmes (AISTATS, 2022)

A Predictive Approach to Bayesian Nonparametric Survival Analysis. - Edwin Fong; Brieuc Lehmann (AISTATS, 2022)

Density Estimation with Autoregressive Bayesian Predictives. - Sahra Ghalebikesabi; Chris C. Holmes; Edwin Fong; Brieuc Lehmann (CoRR, 2022)

Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates. - Sahra Ghalebikesabi; Chris C. Holmes; Edwin Fong; Brieuc Lehmann (UAI, 2023)

Learning from data with structured missingness. - Robin Mitra; Sarah F. McGough; Tapabrata Chakraborti; Chris C. Holmes; Ryan Copping; Niels Hagenbuch; Stefanie Biedermann; Jack Noonan; Brieuc Lehmann; Aditi Shenvi; Xuan Vinh Doan; David Leslie; et al. (Nat. Mac. Intell., 2023)

Fairness Considerations for Conformal Classification. - Arlan Abzhanov; Brieuc Lehmann (COPA, 2024)

A Bayesian multilevel model for populations of networks using exponential-family random graphs. - Brieuc Lehmann; Simon R. White (Stat. Comput., 2024)

Investigating fair data acquisition for risk prediction in resource-constrained settings. - Ioanna Thoma; Elisabeth Abhayaratna; Matthew Sperrin; Karla Diaz-Ordaz; Ricardo Silva; Brieuc Lehmann (EWAF, 2025)

Personalized uncertainty quantification in artificial intelligence. - Tapabrata Chakraborti; Christopher R. S. Banerji; Ariane Marandon; Vicky Hellon; Robin Mitra; Brieuc Lehmann; Leandra Bräuninger; Sarah F. McGough; Cagatay Turkay; Alejandro F. Frangi; Ginestra Bianconi; Weizi Li; et al. (Nat. Mac. Intell., 2025)