Måns Magnusson
Cited by
Cited by
Pulling out the stops: Rethinking stopword removal for topic models
A Schofield, M Magnusson, D Mimno
Proceedings of the 15th Conference of the European Chapter of the …, 2017
Risk of pancreatic cancer among individuals with hepatitis C or hepatitis B virus infection: a nationwide study in Sweden
J Huang, M Magnusson, A Törner, W Ye, AS Duberg
British journal of cancer 109 (11), 2917-2923, 2013
Understanding text pre-processing for latent Dirichlet allocation
A Schofield, M Magnusson, L Thompson, D Mimno
Proceedings of the 15th conference of the European chapter of the …, 2017
Prevailing effectiveness of the 2009 influenza A (H1N1) pdm09 vaccine during the 2010/11 season in Sweden
K Widgren, M Magnusson, P Hagstam, M Widerström, Å Örtqvist, ...
Eurosurveillance 18 (15), 20447, 2013
The incidence of acute gastrointestinal illness in Sweden
FI Hansdotter, M Magnusson, S Kühlmann-Berenzon, A Hulth, ...
Scandinavian Journal of Public Health 43 (5), 540-547, 2015
loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models.(2016)
A Vehtari, A Gelman, J Gabry, Y Yao, PC Bürkner, B Goodrich, J Piironen, ...
URL https://github. com/stan-dev/loo. R package version 1 (0), 6, 0
DOLDA-a regularized supervised topic model for high-dimensional multi-class regression
M Magnusson, L Jonsson, M Villani
arXiv preprint arXiv:1602.00260, 2016
Sparse partially collapsed mcmc for parallel inference in topic models
M Magnusson, L Jonsson, M Villani, D Broman
Journal of Computational and Graphical Statistics 27 (2), 449-463, 2018
Bayesian Leave-One-Out Cross-Validation for Large Data
MR Andersen, M Magnusson, J Jonasson, A Vehtari
36th International Conference on Machine Learning, 7505-7525, 2019
Interpretable Word Embeddings via Informative Priors
MH Bodell, M Arvidsson, M Magnusson
arXiv preprint arXiv:1909.01459, 2019
Leave-one-out cross-validation for Bayesian model comparison in large data
M Magnusson, A Vehtari, J Jonasson, M Andersen
International Conference on Artificial Intelligence and Statistics, 341-351, 2020
Polya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler
A Terenin, M Magnusson, L Jonsson, D Draper
IEEE transactions on pattern analysis and machine intelligence 41 (7), 1709-1719, 2018
Finding the news lead in the data haystack: Automated local data journalism using crime data
M Magnusson, J Finnäs, L Wallentin
Computation+ Journalism Symposium, 2016
Head lice surveillance on a deregulated OTC-sales market: A study using Web query data
J Lindh, M Magnusson, M Grünewald, A Hulth
PLoS One 7 (11), e48666, 2012
Automatic localization of bugs to faulty components in large scale software systems using bayesian classification
L Jonsson, D Broman, M Magnusson, K Sandahl, M Villani, S Eldh
2016 IEEE International Conference on Software Quality, Reliability and …, 2016
Robust, accurate stochastic optimization for variational inference
AK Dhaka, A Catalina, MR Andersen, M Magnusson, JH Huggins, ...
arXiv preprint arXiv:2009.00666, 2020
When are Bayesian model probabilities overconfident?
O Oelrich, S Ding, M Magnusson, A Vehtari, M Villani
arXiv preprint arXiv:2003.04026, 2020
Parallelizing LDA using partially collapsed Gibbs sampling
M Magnusson, L Jonsson, M Villani, D Broman
Statistics 24 (2), 301-327, 2015
Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison
T Sivula, M Magnusson, A Vehtari
arXiv preprint arXiv:2008.10296, 2020
Scalable and Efficient Probabilistic Topic Model Inference for Textual Data
M Magnusson
Linköping University Electronic Press, 2018
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