Learning the solution sparsity of an ill-posed linear inverse problem with the Variational Garrote. - Michael Riis Andersen; Sofie Therese Hansen; Lars Kai Hansen (MLSP, 2013)

Bayesian Inference for Structured Spike and Slab Priors. - Michael Riis Andersen; Ole Winther; Lars Kai Hansen (NIPS, 2014)

EEG source imaging assists decoding in a face recognition task. - Rasmus S. Andersen; Anders U. Eliasen; Nicolai Pedersen; Michael Riis Andersen; Sofie Therese Hansen; Lars Kai Hansen (ICASSP, 2017)

Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. - Michael Riis Andersen; Aki Vehtari; Ole Winther; Lars Kai Hansen (J. Mach. Learn. Res., 2017)

Probabilistic models for structured sparsity. - Michael Riis Andersen (2017)

Bayesian Structure Learning for Dynamic Brain Connectivity. - Michael Riis Andersen; Ole Winther; Lars Kai Hansen; Russell A. Poldrack; Oluwasanmi Koyejo (AISTATS, 2018)

Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations. - Eero Siivola; Aki Vehtari; Jarno Vanhatalo; Javier González 002; Michael Riis Andersen (MLSP, 2018)

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. - Topi Paananen; Juho Piironen; Michael Riis Andersen; Aki Vehtari (AISTATS, 2019)

Unifying Probabilistic Models for Time-frequency Analysis. - William J. Wilkinson; Michael Riis Andersen; Joshua D. Reiss; Dan Stowell; Arno Solin (ICASSP, 2019)

Bayesian leave-one-out cross-validation for large data. - Måns Magnusson; Michael Riis Andersen; Johan Jonasson; Aki Vehtari (ICML, 2019)

End-to-End Probabilistic Inference for Nonstationary Audio Analysis. - William J. Wilkinson; Michael Riis Andersen; Joshua D. Reiss; Dan Stowell; Arno Solin (ICML, 2019)

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. - Måns Magnusson; Aki Vehtari; Johan Jonasson; Michael Riis Andersen (AISTATS, 2020)

State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes. - William J. Wilkinson; Paul E. Chang; Michael Riis Andersen; Arno Solin (ICML, 2020)

Scalable Gaussian Process for Extreme Classification. - Akash Kumar Dhaka; Michael Riis Andersen; Pablo Garcia Moreno; Aki Vehtari (MLSP, 2020)

Robust, Accurate Stochastic Optimization for Variational Inference. - Akash Kumar Dhaka; Alejandro Catalina; Michael Riis Andersen; Måns Magnusson; Jonathan H. Huggins; Aki Vehtari (NeurIPS, 2020)

Preferential Batch Bayesian Optimization. - Eero Siivola; Akash Kumar Dhaka; Michael Riis Andersen; Javier González 002; Pablo Garcia Moreno; Aki Vehtari (MLSP, 2021)

Challenges and Opportunities in High Dimensional Variational Inference. - Akash Kumar Dhaka; Alejandro Catalina; Manushi Welandawe; Michael Riis Andersen; Jonathan H. Huggins; Aki Vehtari (NeurIPS, 2021)

Uncertainty-aware sensitivity analysis using Rényi divergences. - Topi Paananen; Michael Riis Andersen; Aki Vehtari (UAI, 2021)

Robust, Automated, and Accurate Black-box Variational Inference. - Manushi Welandawe; Michael Riis Andersen; Aki Vehtari; Jonathan H. Huggins (CoRR, 2022)

SolarDK: A high-resolution urban solar panel image classification and localization dataset. - Maxim Khomiakov; Julius Holbech Radzikowski; Carl Anton Schmidt; Mathias Bonde Sørensen; Mads Andersen; Michael Riis Andersen; Jes Frellsen (CoRR, 2022)

Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery. - Maxim Khomiakov; Alejandro Valverde Mahou; Alba Reinders Sánchez; Jes Frellsen; Michael Riis Andersen (ICASSP, 2023)

Creating the next generation of news experience on ekstrabladet.dk with recommender systems. - Johannes Kruse 002; Kasper Lindskow; Michael Riis Andersen; Jes Frellsen (RecSys, 2023)

On the role of model uncertainties in Bayesian optimisation. - Jonathan Foldager; Mikkel Jordahn; Lars Kai Hansen; Michael Riis Andersen (UAI, 2023)

Automatic proficiency scoring for early-stage writing. - Michael Riis Andersen; Kristine Kabel; Jesper Bremholm; Jeppe Bundsgaard; Lars Kai Hansen (Comput. Educ. Artif. Intell., 2023)

On the role of Model Uncertainties in Bayesian Optimization. - Jonathan Foldager; Mikkel Jordahn; Lars Kai Hansen; Michael Riis Andersen (CoRR, 2023)

Polygonizer: An auto-regressive building delineator. - Maxim Khomiakov; Michael Riis Andersen; Jes Frellsen (CoRR, 2023)

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. - Gabriel Riutort-Mayol; Paul-Christian Bürkner; Michael Riis Andersen; Arno Solin; Aki Vehtari (Stat. Comput., 2023)

GeoFormer: A Multi-Polygon Segmentation Transformer. - Maxim Khomiakov; Michael Riis Andersen; Jes Frellsen (BMVC, 2024)

Neural machine translation for automated feedback on children's early-stage writing. - Jonas Vestergaard Jensen; Mikkel Jordahn; Michael Riis Andersen (NLDL, 2024)

RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. - Johannes Kruse 002; Kasper Lindskow; Saikishore Kalloori; Marco Polignano; Claudio Pomo; Abhishek Srivastava 004; Anshuk Uppal; Michael Riis Andersen; Jes Frellsen (RecSys, 2024)

EB-NeRD a large-scale dataset for news recommendation. - Johannes Kruse 002; Kasper Lindskow; Saikishore Kalloori; Marco Polignano; Claudio Pomo; Abhishek Srivastava 004; Anshuk Uppal; Michael Riis Andersen; Jes Frellsen (RecSys Challenge, 2024)

Towards Scalable Bayesian Transformers: Investigating stochastic subset selection for NLP. - Peter Johannes Tejlgaard Kampen; Gustav Ragnar Stoettrup Als; Michael Riis Andersen (UAI, 2024)

GAST: Geometry-Aware Structure Transformer. - Maxim Khomiakov; Michael Riis Andersen; Jes Frellsen (WACV (Workshops), 2024)

Variance reduction of diffusion model's gradients with Taylor approximation-based control variate. - Paul Jeha; Will Grathwohl; Michael Riis Andersen; Carl Henrik Ek; Jes Frellsen (CoRR, 2024)

A Framework for Improving the Reliability of Black-box Variational Inference. - Manushi Welandawe; Michael Riis Andersen; Aki Vehtari; Jonathan H. Huggins (J. Mach. Learn. Res., 2024)

On Local Posterior Structure in Deep Ensembles. - Mikkel Jordahn; Jonas Vestergaard Jensen; Mikkel N. Schmidt; Michael Riis Andersen (AISTATS, 2025)