Michael Riis Andersen

51 papers A* 5A 11Misc 3Journal 23Unranked 8
YearRankTypeTitle / Venue / Authors
2026 J jnl
Practical Deep Heteroskedastic Regression.
CoRR
Mikkel Jordahn, Jonas Vestergaard Jensen, James Harrison, Michael Riis Andersen, Mikkel N. Schmidt
2025 A conf
Normative Alignment of Recommender Systems via Internal Label Shift.
RecSys
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Ryotaro Shimizu, Julian J. McAuley, Pierre-Alexandre Mattei, Jes Frellsen
2025 A conf
On Local Posterior Structure in Deep Ensembles.
AISTATS
Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt, Michael Riis Andersen
2025 J jnl
On Local Posterior Structure in Deep Ensembles.
CoRR
Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt, Michael Riis Andersen
2024 J jnl
A Framework for Improving the Reliability of Black-box Variational Inference.
J. Mach. Learn. Res.
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
2024 — conf
EB-NeRD a large-scale dataset for news recommendation.
RecSys Challenge
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen
2024 J jnl
EB-NeRD: A Large-Scale Dataset for News Recommendation.
CoRR
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen
2024 — conf
GAST: Geometry-Aware Structure Transformer.
WACV (Workshops)
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
2024 A conf
GeoFormer: A Multi-Polygon Segmentation Transformer.
BMVC
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
2024 J jnl
GeoFormer: A Multi-Polygon Segmentation Transformer.
CoRR
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
2024 — conf
Neural machine translation for automated feedback on children's early-stage writing.
NLDL
Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen
2024 A conf
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations.
RecSys
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen
2024 J jnl
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations.
CoRR
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen
2024 A conf
Towards Scalable Bayesian Transformers: Investigating stochastic subset selection for NLP.
UAI
Peter Johannes Tejlgaard Kampen, Gustav Ragnar Stoettrup Als, Michael Riis Andersen
2024 J jnl
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate.
CoRR
Paul Jeha, Will Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen
2023 J jnl
Automatic proficiency scoring for early-stage writing.
Comput. Educ. Artif. Intell.
Michael Riis Andersen, Kristine Kabel, Jesper Bremholm, Jeppe Bundsgaard, Lars Kai Hansen
2023 A conf
Creating the next generation of news experience on ekstrabladet.dk with recommender systems.
RecSys
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Jes Frellsen
2023 Misc conf
Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery.
ICASSP
Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen, Michael Riis Andersen
2023 J jnl
Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery.
CoRR
Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen, Michael Riis Andersen
2023 J jnl
Neural machine translation for automated feedback on children's early-stage writing.
CoRR
Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen
2023 J jnl
On the role of Model Uncertainties in Bayesian Optimization.
CoRR
Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen
2023 A conf
On the role of model uncertainties in Bayesian optimisation.
UAI
Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen
2023 J jnl
Polygonizer: An auto-regressive building delineator.
CoRR
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
2023 J jnl
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming.
Stat. Comput.
Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael Riis Andersen, Arno Solin, Aki Vehtari
2022 J jnl
Robust, Automated, and Accurate Black-box Variational Inference.
CoRR
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
2022 J jnl
SolarDK: A high-resolution urban solar panel image classification and localization dataset.
CoRR
Maxim Khomiakov, Julius Holbech Radzikowski, Carl Anton Schmidt, Mathias Bonde Sørensen, Mads Andersen, Michael Riis Andersen, Jes Frellsen
2021 A* conf
Challenges and Opportunities in High Dimensional Variational Inference.
NeurIPS
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
2021 J jnl
Challenges and Opportunities in High-dimensional Variational Inference.
CoRR
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
2021 — conf
Preferential Batch Bayesian Optimization.
MLSP
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari
2021 A conf
Uncertainty-aware sensitivity analysis using Rényi divergences.
UAI
Topi Paananen, Michael Riis Andersen, Aki Vehtari
2020 A conf
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data.
AISTATS
Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen
2020 J jnl
Preferential Batch Bayesian Optimization.
CoRR
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari
2020 A* conf
Robust, Accurate Stochastic Optimization for Variational Inference.
NeurIPS
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
2020 J jnl
Robust, Accurate Stochastic Optimization for Variational Inference.
CoRR
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
2020 — conf
Scalable Gaussian Process for Extreme Classification.
MLSP
Akash Kumar Dhaka, Michael Riis Andersen, Pablo Garcia Moreno, Aki Vehtari
2020 A* conf
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes.
ICML
William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
2020 J jnl
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes.
CoRR
William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
2019 A* conf
Bayesian leave-one-out cross-validation for large data.
ICML
Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
2019 J jnl
Bayesian leave-one-out cross-validation for large data.
CoRR
Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
2019 A* conf
End-to-End Probabilistic Inference for Nonstationary Audio Analysis.
ICML
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
2019 J jnl
End-to-End Probabilistic Inference for Nonstationary Audio Analysis.
CoRR
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
2019 Misc conf
Unifying Probabilistic Models for Time-frequency Analysis.
ICASSP
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
2019 A conf
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution.
AISTATS
Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari
2018 A conf
Bayesian Structure Learning for Dynamic Brain Connectivity.
AISTATS
Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo
2018 — conf
Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations.
MLSP
Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González, Michael Riis Andersen
2018 J jnl
Unifying Probabilistic Models for Time-Frequency Analysis.
CoRR
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
2017 J jnl
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors.
J. Mach. Learn. Res.
Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen
2017 Misc conf
EEG source imaging assists decoding in a face recognition task.
ICASSP
Rasmus S. Andersen, Anders U. Eliasen, Nicolai Pedersen, Michael Riis Andersen, Sofie Therese Hansen, Lars Kai Hansen
2017 — —
Probabilistic models for structured sparsity.
Michael Riis Andersen
2014 — conf
Bayesian Inference for Structured Spike and Slab Priors.
NIPS
Michael Riis Andersen, Ole Winther, Lars Kai Hansen
2013 — conf
Learning the solution sparsity of an ill-posed linear inverse problem with the Variational Garrote.
MLSP
Michael Riis Andersen, Sofie Therese Hansen, Lars Kai Hansen