7 papers
Bivariate geostatistical latent variable models for the analysis of antibody density data
Emanuele Giorgi, Jonas Wallin
The increasing availability of serosurveys that measure antibody responses to multiple antigens requires the development of methods that can exploit the full information content of…
Scalable Ultra-High-Dimensional Quantile Regression with Genomic Applications
Hanqing Wu, Jonas Wallin, Iuliana Ionita-Laza
Modern datasets arising from social media, genomics, and biomedical informatics are often heterogeneous and (ultra) high-dimensional, creating substantial challenges for convention…
A flexible class of latent variable models for the analysis of antibody response data
Emanuele Giorgi, Jonas Wallin
Existing approaches to modelling antibody concentration data are mostly based on finite mixture models that rely on the assumption that individuals can be divided into two distinct…
Controllable protein design with particle-based Feynman-Kac steering
Erik Hartman, Jonas Wallin, Johan Malmström +1
Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology. Diffusion-based generati…
Identifying Network Hubs with the Partial Correlation Graphical LASSO
Małgorzata Bogdan, Adam Chojecki, Ivan Hejný +2
Graphical LASSO (GLASSO) is a widely used method for estimating sparse precision matrices and learning undirected graphical models in high-dimensional settings. Because GLASSO pena…
Asymptotic Distribution of Low-Dimensional Patterns Induced by Non-Differentiable Regularizers under General Loss Functions
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan
This article investigates the asymptotic distribution of penalized estimators with non-differentiable penalties designed to recover low-dimensional pattern structures. Patterns pla…
Unveiling low-dimensional patterns induced by convex non-differentiable regularizers
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan +1
Popular regularizers with non-differentiable penalties, such as Lasso, Elastic Net, Generalized Lasso, or SLOPE, reduce the dimension of the parameter space by inducing sparsity or…