Mode jumping MCMC for Bayesian variable selection in GLMM. - Aliaksandr Hubin; Geir Storvik (Comput. Stat. Data Anal., 2018)

An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models. - Aliaksandr Hubin (AIIPCC, 2019)

Combining Model and Parameter Uncertainty in Bayesian Neural Networks. - Aliaksandr Hubin; Geir Storvik (CoRR, 2019)

Named Entity Recognition without Labelled Data: A Weak Supervision Approach. - Pierre Lison; Jeremy Barnes 001; Aliaksandr Hubin; Samia Touileb (ACL, 2020)

skweak: Weak Supervision Made Easy for NLP. - Pierre Lison; Jeremy Barnes 001; Aliaksandr Hubin (ACL (demo), 2021)

Flexible Bayesian Nonlinear Model Configuration. - Aliaksandr Hubin; Geir Storvik; Florian Frommlet (J. Artif. Intell. Res., 2021)

A subsampling approach for Bayesian model selection. - Jon Lachmann; Geir Storvik; Florian Frommlet; Aliaksandr Hubin (Int. J. Approx. Reason., 2022)

Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty. - Aliaksandr Hubin; Geir Storvik (CoRR, 2023)

Incorporating probabilistic domain knowledge into deep multiple instance learning. - Ghadi S. Al Hajj; Aliaksandr Hubin; Chakravarthi Kanduri; Milena Pavlovic; Knut Dagestad Rand; Michael Widrich; Anne H. Schistad Solberg; Victor Greiff; Johan Pensar; Günter Klambauer; Geir Kjetil Sandve (ICML, 2024)

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)

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)

Sparsifying Bayesian neural networks with latent binary variables and normalizing flows. - Lars Skaaret-Lund; Geir Storvik; Aliaksandr Hubin (Trans. Mach. Learn. Res., 2024)

Evolutionary variational inference for Bayesian generalized nonlinear models. - Philip Sebastian Hauglie Sommerfelt; Aliaksandr Hubin (Neural Comput. Appl., 2024)

Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks. - Eirik Høyheim; Lars Skaaret-Lund; Solve Sæbø; Aliaksandr Hubin (CoRR, 2025)

The effect of stereotypes on perceived competence of indigenous software practitioners: a study of dress style in professional photos. - Mary Sánchez-Gordón; Ricardo Colomo-Palacios; Cathy Pamela Guevara-Vega; Antonio Quiña-Mera; Aliaksandr Hubin (Empir. Softw. Eng., 2025)