Michael Riis Andersen

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 34

Top Topics

Gaussian Process

Top Venues

Publications

Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen
Neural machine translation for automated feedback on children's early-stage writing. NLDL (2024)
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
GAST: Geometry-Aware Structure Transformer. WACV (Workshops) (2024)
Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen
On the role of Model Uncertainties in Bayesian Optimization. CoRR (2023)
Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen
Neural machine translation for automated feedback on children's early-stage writing. CoRR (2023)
Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen, Michael Riis Andersen
Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery. ICASSP (2023)
Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen
On the role of model uncertainties in Bayesian optimisation. UAI (2023)
Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen, Michael Riis Andersen
Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery. CoRR (2023)
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Jes Frellsen
Creating the next generation of news experience on ekstrabladet.dk with recommender systems. RecSys (2023)
Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael Riis Andersen, Arno Solin, Aki Vehtari
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Stat. Comput. 33 (1) (2023)
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
Polygonizer: An auto-regressive building delineator. CoRR (2023)
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
Robust, Automated, and Accurate Black-box Variational Inference. CoRR (2022)
Maxim Khomiakov, Julius Holbech Radzikowski, Carl Anton Schmidt, Mathias Bonde Sørensen, Mads Andersen, Michael Riis Andersen, Jes Frellsen
SolarDK: A high-resolution urban solar panel image classification and localization dataset. CoRR (2022)
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari
Preferential Batch Bayesian Optimization. MLSP (2021)
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
Challenges and Opportunities in High-dimensional Variational Inference. CoRR (2021)
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
Challenges and Opportunities in High Dimensional Variational Inference. NeurIPS (2021)
Topi Paananen, Michael Riis Andersen, Aki Vehtari
Uncertainty-aware sensitivity analysis using Rényi divergences. UAI (2021)
William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes. CoRR (2020)
Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. AISTATS (2020)
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
Robust, Accurate Stochastic Optimization for Variational Inference. CoRR (2020)
Akash Kumar Dhaka, Michael Riis Andersen, Pablo Garcia Moreno, Aki Vehtari
Scalable Gaussian Process for Extreme Classification. MLSP (2020)
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier Gonzalez, Pablo Garcia Moreno, Aki Vehtari
Preferential Batch Bayesian Optimization. CoRR (2020)
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS (2020)
William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes. ICML (2020)
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
Unifying Probabilistic Models for Time-frequency Analysis. ICASSP (2019)
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
End-to-End Probabilistic Inference for Nonstationary Audio Analysis. CoRR (2019)
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
End-to-End Probabilistic Inference for Nonstationary Audio Analysis. ICML (2019)
Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
Bayesian leave-one-out cross-validation for large data. ICML (2019)
Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
Bayesian leave-one-out cross-validation for large data. CoRR (2019)
Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. AISTATS (2019)
Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier Gonzalez, Michael Riis Andersen
Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations. MLSP (2018)
Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo
Bayesian Structure Learning for Dynamic Brain Connectivity. AISTATS (2018)
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
Unifying Probabilistic Models for Time-Frequency Analysis. CoRR (2018)
Rasmus S. Andersen, Anders U. Eliasen, Nicolai Pedersen, Michael Riis Andersen, Sofie Therese Hansen, Lars Kai Hansen
EEG source imaging assists decoding in a face recognition task. ICASSP (2017)
Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. J. Mach. Learn. Res. 18 (2017)
Michael Riis Andersen, Ole Winther, Lars Kai Hansen
Bayesian Inference for Structured Spike and Slab Priors. NIPS (2014)
Michael Riis Andersen, Sofie Therese Hansen, Lars Kai Hansen
Learning the solution sparsity of an ill-posed linear inverse problem with the Variational Garrote. MLSP (2013)