Michael U. Gutmann
Cited by
Cited by
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M Gutmann, A Hyvärinen
Proceedings of the thirteenth international conference on artificial …, 2010
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics.
MU Gutmann, A Hyvärinen
Journal of machine learning research 13 (2), 2012
Veegan: Reducing mode collapse in gans using implicit variational learning
A Srivastava, L Valkov, C Russell, MU Gutmann, C Sutton
Advances in neural information processing systems 30, 2017
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
MU Gutmann, J Corander
Journal of Machine Learning Research 17, 1-47, 2016
Fundamentals and recent developments in approximate Bayesian computation
J Lintusaari, MU Gutmann, R Dutta, S Kaski, J Corander
Systematic biology 66 (1), e66-e82, 2017
Genome-wide CRISPR screen identifies host dependency factors for influenza A virus infection
B Li, SM Clohisey, BS Chia, B Wang, A Cui, T Eisenhaure, LD Schweitzer, ...
Nature communications 11 (1), 164, 2020
Likelihood-free inference by ratio estimation
O Thomas, R Dutta, J Corander, S Kaski, MU Gutmann
Bayesian Analysis 17 (1), 1-31, 2022
Likelihood-free inference via classification
MU Gutmann, R Dutta, S Kaski, J Corander
Statistics and Computing 28, 411-425, 2018
Frequency-dependent selection in vaccine-associated pneumococcal population dynamics
J Corander, C Fraser, MU Gutmann, B Arnold, WP Hanage, SD Bentley, ...
Nature ecology & evolution 1 (12), 1950-1960, 2017
Bayesian inference of atomistic structure in functional materials
M Todorović, MU Gutmann, J Corander, P Rinke
Npj computational materials 5 (1), 35, 2019
Telescoping density-ratio estimation
B Rhodes, K Xu, MU Gutmann
Advances in neural information processing systems 33, 4905-4916, 2020
Elfi: Engine for likelihood-free inference
J Lintusaari, H Vuollekoski, A Kangasrääsiö, K Skytén, M Järvenpää, ...
Journal of Machine Learning Research 19 (16), 1-7, 2018
Bregman divergence as general framework to estimate unnormalized statistical models
M Gutmann, J Hirayama
arXiv preprint arXiv:1202.3727, 2012
Efficient acquisition rules for model-based approximate Bayesian computation
M Järvenpää, MU Gutmann, A Pleska, A Vehtari, P Marttinen
Bayesian experimental design for implicit models by mutual information neural estimation
S Kleinegesse, MU Gutmann
International conference on machine learning, 5316-5326, 2020
Neural approximate sufficient statistics for implicit models
Y Chen, D Zhang, M Gutmann, A Courville, Z Zhu
arXiv preprint arXiv:2010.10079, 2020
Efficient Bayesian experimental design for implicit models
S Kleinegesse, MU Gutmann
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2
A Hyvärinen, M Gutmann, PO Hoyer
BMC neuroscience 6, 1-12, 2005
Weak epistasis may drive adaptation in recombining bacteria
BJ Arnold, MU Gutmann, YH Grad, SK Sheppard, J Corander, M Lipsitch, ...
Genetics 208 (3), 1247-1260, 2018
Conditional noise-contrastive estimation of unnormalised models
C Ceylan, MU Gutmann
International Conference on Machine Learning, 726-734, 2018
The system can't perform the operation now. Try again later.
Articles 1–20