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Michael Riis Andersen
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2020 – today
- 2024
- [j3]Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
A Framework for Improving the Reliability of Black-box Variational Inference. J. Mach. Learn. Res. 25: 219:1-219:71 (2024) - [c23]Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen:
Neural machine translation for automated feedback on children's early-stage writing. NLDL 2024: 104-112 - [c22]Johannes Kruse
, Kasper Lindskow
EB-NeRD a large-scale dataset for news recommendation. RecSys Challenge 2024: 1-11 - [c21]Johannes Kruse
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. RecSys 2024: 1195-1199 - [c20]Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen:
GAST: Geometry-Aware Structure Transformer. WACV (Workshops) 2024: 776-784 - [i16]Paul Jeha, Will Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen:
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate. CoRR abs/2408.12270 (2024) - [i15]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. CoRR abs/2409.20483 (2024) - [i14]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
EB-NeRD: A Large-Scale Dataset for News Recommendation. CoRR abs/2410.03432 (2024) - 2023
- [j2]Gabriel Riutort-Mayol
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Stat. Comput. 33(1): 17 (2023) - [c19]Maxim Khomiakov, Alejandro Valverde Mahou
Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery. ICASSP 2023: 1-5 - [c18]Johannes Kruse
Creating the next generation of news experience on ekstrabladet.dk with recommender systems. RecSys 2023: 1067-1070 - [c17]Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen:
On the role of model uncertainties in Bayesian optimisation. UAI 2023: 592-601 - [i13]Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen:
On the role of Model Uncertainties in Bayesian Optimization. CoRR abs/2301.05983 (2023) - [i12]Maxim Khomiakov
Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery. CoRR abs/2303.11215 (2023) - [i11]Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen:
Polygonizer: An auto-regressive building delineator. CoRR abs/2304.04048 (2023) - [i10]Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen:
Neural machine translation for automated feedback on children's early-stage writing. CoRR abs/2311.09389 (2023) - 2022
- [i9]Manushi Welandawe
Robust, Automated, and Accurate Black-box Variational Inference. CoRR abs/2203.15945 (2022) - [i8]Maxim Khomiakov
SolarDK: A high-resolution urban solar panel image classification and localization dataset. CoRR abs/2212.01260 (2022) - 2021
- [c16]Eero Siivola
Preferential Batch Bayesian Optimization. MLSP 2021: 1-6 - [c15]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: 7787-7798 - [c14]Topi Paananen, Michael Riis Andersen, Aki Vehtari:
Uncertainty-aware sensitivity analysis using Rényi divergences. UAI 2021: 1185-1194 - [i7]Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe
Challenges and Opportunities in High-dimensional Variational Inference. CoRR abs/2103.01085 (2021) - 2020
- [c13]Måns Magnusson, Aki Vehtari, Johan Jonasson, Michael Riis Andersen:
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. AISTATS 2020: 341-351 - [c12]William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin:
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes. ICML 2020: 10270-10281 - [c11]Akash Kumar Dhaka, Michael Riis Andersen, Pablo Garcia Moreno, Aki Vehtari
Scalable Gaussian Process for Extreme Classification. MLSP 2020: 1-6 - [c10]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS 2020 - [i6]Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen, Javier González, Pablo Garcia Moreno, Aki Vehtari:
Preferential Batch Bayesian Optimization. CoRR abs/2003.11435 (2020) - [i5]William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin:
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes. CoRR abs/2007.05994 (2020) - [i4]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. CoRR abs/2009.00666 (2020)
2010 – 2019
- 2019
- [c9]Topi Paananen, Juho Piironen
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. AISTATS 2019: 1743-1752 - [c8]William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell
Unifying Probabilistic Models for Time-frequency Analysis. ICASSP 2019: 3352-3356 - [c7]Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari:
Bayesian leave-one-out cross-validation for large data. ICML 2019: 4244-4253 - [c6]William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin:
End-to-End Probabilistic Inference for Nonstationary Audio Analysis. ICML 2019: 6776-6785 - [i3]William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin:
End-to-End Probabilistic Inference for Nonstationary Audio Analysis. CoRR abs/1901.11436 (2019) - [i2]Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari:
Bayesian leave-one-out cross-validation for large data. CoRR abs/1904.10679 (2019) - 2018
- [c5]Michael Riis Andersen, Ole Winther, Lars Kai Hansen
Bayesian Structure Learning for Dynamic Brain Connectivity. AISTATS 2018: 1436-1446 - [c4]Eero Siivola
Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations. MLSP 2018: 1-6 - [i1]William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin:
Unifying Probabilistic Models for Time-Frequency Analysis. CoRR abs/1811.02489 (2018) - 2017
- [b1]Michael Riis Andersen:
Probabilistic models for structured sparsity. Technical University of Denmark, 2017 - [j1]Michael Riis Andersen, Aki Vehtari, Ole Winther, Lars Kai Hansen
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. J. Mach. Learn. Res. 18: 139:1-139:58 (2017) - [c3]Rasmus S. Andersen, Anders U. Eliasen, Nicolai Pedersen
EEG source imaging assists decoding in a face recognition task. ICASSP 2017: 939-943 - 2014
- [c2]Michael Riis Andersen, Ole Winther, Lars Kai Hansen
Bayesian Inference for Structured Spike and Slab Priors. NIPS 2014: 1745-1753 - 2013
- [c1]Michael Riis Andersen, Sofie Therese Hansen
Learning the solution sparsity of an ill-posed linear inverse problem with the Variational Garrote. MLSP 2013: 1-6
Coauthor Index
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last updated on 2024-11-13 23:53 CET by the dblp team
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