Michael U. Gutmann
Cited by
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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
Proceedings of the 31st International Conference on Neural Information …, 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
Likelihood-free inference via classification
MU Gutmann, R Dutta, S Kaski, J Corander
Statistics and Computing 28 (2), 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
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), 1-18, 2020
Likelihood-free inference by ratio estimation
O Thomas, R Dutta, J Corander, S Kaski, MU Gutmann
arXiv preprint arXiv:1611.10242, 2016
Bayesian inference of atomistic structure in functional materials
M Todorović, MU Gutmann, J Corander, P Rinke
Npj computational materials 5 (1), 1-7, 2019
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 Analysis 14 (2), 595-622, 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), 1-12, 2005
Elfi: Engine for likelihood-free inference
J Lintusaari, H Vuollekoski, A Kangasraasio, K Skytén, M Jarvenpaa, ...
Journal of Machine Learning Research 19 (16), 1-7, 2018
Recombination produces coherent bacterial species clusters in both core and accessory genomes
P Marttinen, NJ Croucher, MU Gutmann, J Corander, WP Hanage
Microbial Genomics 1 (5), 2015
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
S Liu, JA Quinn, MU Gutmann, M Sugiyama
European Conference on Machine Learning and Principles and Practice of …, 2013
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
A family of computationally efficient and simple estimators for unnormalized statistical models
M Pihlaja, M Gutmann, A Hyvarinen
arXiv preprint arXiv:1203.3506, 2012
Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
M Järvenpää, MU Gutmann, A Vehtari, P Marttinen
The Annals of Applied Statistics 12 (4), 2228-2251, 2018
Adaptable pouring: Teaching robots not to spill using fast but approximate fluid simulation
TL Guevara, NK Taylor, MU Gutmann, S Ramamoorthy, K Subr
Proceedings of the Conference on Robot Learning (CoRL), 2017
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Articles 1–20