Michael U. Gutmann

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Aapo HyvarinenUniversity of HelsinkiVerified email at helsinki.fi
Jukka CoranderUiO, Wellcome Sanger Institute, University of HelsinkiVerified email at medisin.uio.no
Samuel KaskiProfessor, Aalto University and University of ManchesterVerified email at aalto.fi
Akash SrivastavaMIT, IBM Research, University of EdinburghVerified email at mit.edu
Jarno LintusaariAalto UniversityVerified email at aalto.fi
Aki VehtariProfessor, Aalto UniversityVerified email at aalto.fi
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
Steven KleinegesseLead Research Scientist in Machine Learning, causaLensVerified email at causalens.com
William HanageHarvard T H Chan School of Public HealthVerified email at hsph.harvard.edu
Ben RhodesPhD student, University of EdinburghVerified email at ed.ac.uk
Kenneth BaillieProfessor of Experimental Medicine, University of EdinburghVerified email at ed.ac.uk
Kai XuResearch Scientist, MIT-IBM Watson AI LabVerified email at mit.edu
Marc LipsitchDepartment of Epidemiology, Harvard School of Public HealthVerified email at hsph.harvard.edu
Brian J ArnoldPrinceton UniversityVerified email at princeton.edu
John QuinnGoogle Research, Sunbird AI, Makerere UniversityVerified email at cit.ac.ug
Nick CroucherImperial CollegeVerified email at imperial.ac.uk
Kartic SubrUniversity of EdinburghVerified email at ed.ac.uk
Subramanian RamamoorthyProfessor of Robot Learning and Autonomy, School of Informatics, University of EdinburghVerified email at ed.ac.uk
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Song LiuAssociate Professor, University of Bristol, UKVerified email at bristol.ac.uk

Michael U. Gutmann

University of Edinburgh

Verified email at ed.ac.uk - Homepage

Machine Learning Bayesian Computation Computational Biology


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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	2723	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	938	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	813	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	347	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	302	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	182	2020
Likelihood-free inference by ratio estimation O Thomas, R Dutta, J Corander, S Kaski, MU Gutmann Bayesian Analysis 17 (1), 1-31, 2022	166*	2022
Likelihood-free inference via classification MU Gutmann, R Dutta, S Kaski, J Corander Statistics and Computing 28, 411-425, 2018	152*	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	139	2017
Bayesian inference of atomistic structure in functional materials M Todorović, MU Gutmann, J Corander, P Rinke Npj computational materials 5 (1), 35, 2019	127	2019
Telescoping density-ratio estimation B Rhodes, K Xu, MU Gutmann Advances in neural information processing systems 33, 4905-4916, 2020	101	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	94	2018
Bregman divergence as general framework to estimate unnormalized statistical models M Gutmann, J Hirayama arXiv preprint arXiv:1202.3727, 2012	86	2012
Efficient acquisition rules for model-based approximate Bayesian computation M Järvenpää, MU Gutmann, A Pleska, A Vehtari, P Marttinen	81	2019
Bayesian experimental design for implicit models by mutual information neural estimation S Kleinegesse, MU Gutmann International conference on machine learning, 5316-5326, 2020	80	2020
Neural approximate sufficient statistics for implicit models Y Chen, D Zhang, M Gutmann, A Courville, Z Zhu arXiv preprint arXiv:2010.10079, 2020	62	2020
Efficient Bayesian experimental design for implicit models S Kleinegesse, MU Gutmann The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	59	2019
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	56	2018
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	55	2005
Conditional noise-contrastive estimation of unnormalised models C Ceylan, MU Gutmann International Conference on Machine Learning, 726-734, 2018	53	2018

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