Michael U. Gutmann

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Aapo HyvarinenUniversity of HelsinkiVerified email at helsinki.fi
Jarno LintusaariAalto UniversityVerified email at aalto.fi
Aki VehtariAssociate Professor, Aalto UniversityVerified email at aalto.fi
William HanageHarvard T H Chan School of Public HealthVerified email at hsph.harvard.edu
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
Marko JärvenpääPostdoc, University of OsloVerified email at medisin.uio.no
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
Nick CroucherImperial CollegeVerified email at imperial.ac.uk
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
John QuinnGoogle Research, Sunbird AI, 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
Samuel K. SheppardThe Milner Centre for Evolution, University of Bath, United KingdomVerified email at bath.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
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 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	1320	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	694	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	467	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	231	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	211	2017
Likelihood-free inference via classification MU Gutmann, R Dutta, S Kaski, J Corander Statistics and Computing 28 (2), 411-425, 2018	118*	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	103	2017
Likelihood-free inference by ratio estimation O Thomas, R Dutta, J Corander, S Kaski, MU Gutmann arXiv preprint arXiv:1611.10242, 2016	88*	2016
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	82	2020
Bayesian inference of atomistic structure in functional materials M Todorović, MU Gutmann, J Corander, P Rinke Npj computational materials 5 (1), 1-7, 2019	65	2019
Bregman divergence as general framework to estimate unnormalized statistical models M Gutmann, J Hirayama arXiv preprint arXiv:1202.3727, 2012	60	2012
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	55*	2018
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	53	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	49	2005
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	45	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	45	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	40	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	36	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	32	2018
Efficient bayesian experimental design for implicit models S Kleinegesse, MU Gutmann The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	27	2019

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