17 citations · 55 across the 9 of their papers we have counts for
10 papers
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson, Jakob Torgander, Paul-Christian Bürkner +3
The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When de…
Probabilistic Embeddings with Laplacian Graph Priors
Väinö Yrjänäinen, Måns Magnusson
We introduce probabilistic embeddings using Laplacian priors (PELP). The proposed model enables incorporating graph side-information into static word embeddings. We theoretically s…
Unbiased estimator for the variance of the leave-one-out cross-validation estimator for a Bayesian normal model with fixed variance
Tuomas Sivula, Måns Magnusson, Aki Vehtari
When evaluating and comparing models using leave-one-out cross-validation (LOO-CV), the uncertainty of the estimate is typically assessed using the variance of the sampling distrib…
Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison
Tuomas Sivula, Måns Magnusson, Asael Alonzo Matamoros +1
It is useful to estimate the expected predictive performance of models planned to be used for prediction. We focus on leave-one-out cross-validation (LOO-CV), which has become a po…
Interpretable Word Embeddings via Informative Priors
Miriam Hurtado Bodell, Martin Arvidsson, Måns Magnusson
Word embeddings have demonstrated strong performance on NLP tasks. However, lack of interpretability and the unsupervised nature of word embeddings have limited their use within co…
Bayesian leave-one-out cross-validation for large data
Måns Magnusson, Michael Riis Andersen, Johan Jonasson +1
Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approac…
Multivariate Analysis of Orthogonal Range Searching and Graph Distances Parameterized by Treewidth
Karl Bringmann, Thore Husfeldt, Måns Magnusson
We show that the eccentricities, diameter, radius, and Wiener index of an undirected -vertex graph with nonnegative edge lengths can be computed in time $O(n\cdot \binom{k+\lcei…
Pólya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler
Alexander Terenin, Måns Magnusson, Leif Jonsson +1
Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorith…
DOLDA - a regularized supervised topic model for high-dimensional multi-class regression
Måns Magnusson, Leif Jonsson, Mattias Villani
Generating user interpretable multi-class predictions in data rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Laten…
Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
Måns Magnusson, Leif Jonsson, Mattias Villani +1
Topic models, and more specifically the class of Latent Dirichlet Allocation (LDA), are widely used for probabilistic modeling of text. MCMC sampling from the posterior distributio…