Michael U. Gutmann - Google Scholar

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	All	Since 2015
Citations	2427	2205
h-index	21	17
i10-index	31	28

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20062007200820092010201120122013201420152016201720182019202010 4 9 12 12 10 24 44 74 95 204 267 440 566 618

Co-authors

Aapo HyvarinenUniversity of HelsinkiVerified email at helsinki.fi
Jarno LintusaariAalto UniversityVerified email at aalto.fi
William HanageHarvard T H Chan School of Public HealthVerified email at hsph.harvard.edu
Aki VehtariAssociate Professor, Aalto UniversityVerified email at aalto.fi
Marko JärvenpääResearcher, University of OsloVerified email at medisin.uio.no
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
Akash SrivastavaMIT, IBM Research, University of EdinburghVerified email at mit.edu
Marc LipsitchDepartment of Epidemiology, Harvard School of Public HealthVerified email at hsph.harvard.edu
Brian J ArnoldHarvard UniversityVerified email at g.harvard.edu
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
John QuinnGoogle AI Ghana, Makerere UniversityVerified email at cit.ac.ug
Song LiuLecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Shouno HayaruUniversity of Electro-CommunicationsVerified email at uec.ac.jp
Nick CroucherImperial CollegeVerified email at imperial.ac.uk
Kartic SubrUniversity of EdinburghVerified email at ed.ac.uk
Subramanian RamamoorthySchool of Informatics, The University of Edinburgh; FiveAI Inc.Verified email at ed.ac.uk
Samuel K. SheppardThe Milner Centre for Evolution, University of Bath, United KingdomVerified email at bath.ac.uk
Jesus MaloProfessor of Vision Science, Universitat de ValenciaVerified email at uv.es
Valero LaparraUniversitat de ValènciaVerified email at uv.es
Christophe FraserOxford Big Data Institute, Nuffield Department of Medicine, University of OxfordVerified email at bdi.ox.ac.uk

Michael U. Gutmann

University of Edinburgh

Verified email at ed.ac.uk - Homepage

Machine Learning Contrastive 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	596	2010
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics MU Gutmann, A Hyvärinen The journal of machine learning research 13 (1), 307-361, 2012	498	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, 3308-3318, 2017	234	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	169	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	133	2017
Likelihood-free inference via classification MU Gutmann, R Dutta, S Kaski, J Corander Statistics and Computing 28 (2), 411-425, 2018	84*	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	70	2017
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	46	2005
Bregman divergence as general framework to estimate unnormalized statistical models M Gutmann, J Hirayama arXiv preprint arXiv:1202.3727, 2012	39	2012
Likelihood-free inference by ratio estimation O Thomas, R Dutta, J Corander, S Kaski, MU Gutmann Bayesian Analysis, 2020	38*	2020
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	38	2015
Elfi: Engine for likelihood-free inference J Lintusaari, H Vuollekoski, A Kangasrääsiö, K Skytén, M Järvenpää, ... The Journal of Machine Learning Research 19 (1), 643-649, 2018	36*	2018
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	36	2013
Bayesian inference of atomistic structure in functional materials M Todorović, MU Gutmann, J Corander, P Rinke Npj computational materials 5 (1), 1-7, 2019	33	2019
A family of computationally efficient and simple estimators for unnormalized statistical models M Pihlaja, M Gutmann, A Hyvarinen arXiv preprint arXiv:1203.3506, 2012	31	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	30	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	27	2018
Statistical models of images and early vision M Gutmann	22	2005
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	21	2018
Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images MU Gutmann, V Laparra, A Hyvärinen, J Malo PloS one 9 (2), e86481, 2014	21	2014

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