Aliaksandr Hubin

According to our database, Aliaksandr Hubin authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Evolutionary variational inference for Bayesian generalized nonlinear models.

Philip Sebastian Hauglie Sommerfelt, Aliaksandr Hubin

Neural Comput. Appl., November, 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

CoRR, 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Sparsifying Bayesian neural networks with latent binary variables and normalizing flows.

Lars Skaaret-Lund, Geir Storvik, Aliaksandr Hubin

CoRR, 2023

Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty.

Aliaksandr Hubin, Geir Storvik

CoRR, 2023

2022

A subsampling approach for Bayesian model selection.

Jon Lachmann, Geir Storvik, Florian Frommlet, Aliaksandr Hubin

Int. J. Approx. Reason., 2022

2021

Flexible Bayesian Nonlinear Model Configuration.

Aliaksandr Hubin, Geir Storvik, Florian Frommlet

J. Artif. Intell. Res., 2021

skweak: Weak Supervision Made Easy for NLP.

Pierre Lison, Jeremy Barnes, Aliaksandr Hubin

Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

Named Entity Recognition without Labelled Data: A Weak Supervision Approach.

Pierre Lison, Jeremy Barnes, Aliaksandr Hubin, Samia Touileb

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

Combining Model and Parameter Uncertainty in Bayesian Neural Networks.

Aliaksandr Hubin, Geir Storvik

CoRR, 2019

An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models.

Aliaksandr Hubin

Proceedings of the International Conference on Artificial Intelligence, 2019

2018

Mode jumping MCMC for Bayesian variable selection in GLMM.

Aliaksandr Hubin, Geir Storvik

Comput. Stat. Data Anal., 2018