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
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
Akash SrivastavaMIT, IBM Research, University of EdinburghVerified email at mit.edu
William HanageHarvard T H Chan School of Public HealthVerified email at hsph.harvard.edu
Steven KleinegesseData Science PhD Student, University of EdinburghVerified email at sms.ed.ac.uk
Marko JärvenpääPostdoc, University of OsloVerified email at medisin.uio.no
Ben RhodesPhD student, University of EdinburghVerified email at ed.ac.uk
Kenneth BaillieProfessor of Experimental Medicine, University of EdinburghVerified email at ed.ac.uk
Marc LipsitchDepartment of Epidemiology, Harvard School of Public HealthVerified email at hsph.harvard.edu
Brian J ArnoldHarvard UniversityVerified email at g.harvard.edu
Kai Xu / 徐锴Research Scientist, AmazonVerified email at amazon.com
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 LiuSenior Lecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Shouno HayaruUniversity of Electro-CommunicationsVerified email at uec.ac.jp
Samuel K. SheppardDepartment of Biology, Oxford University, UKVerified email at biology.ox.ac.uk
Kartic SubrUniversity of EdinburghVerified email at ed.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	1901	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	802	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	634	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	286	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	246	2017
Likelihood-free inference via classification MU Gutmann, R Dutta, S Kaski, J Corander Statistics and Computing 28, 411-425, 2018	140*	2018
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	128	2020
Likelihood-free inference by ratio estimation O Thomas, R Dutta, J Corander, S Kaski, MU Gutmann Bayesian Analysis 17 (1), 1-31, 2022	122*	2022
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	116	2017
Bayesian inference of atomistic structure in functional materials M Todorović, MU Gutmann, J Corander, P Rinke Npj computational materials 5 (1), 35, 2019	87	2019
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	71*	2018
Bregman divergence as general framework to estimate unnormalized statistical models M Gutmann, J Hirayama arXiv preprint arXiv:1202.3727, 2012	67	2012
Efficient acquisition rules for model-based approximate Bayesian computation M Järvenpää, MU Gutmann, A Pleska, A Vehtari, P Marttinen	64	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	52	2005
Bayesian experimental design for implicit models by mutual information neural estimation S Kleinegesse, MU Gutmann International Conference on Machine Learning, 5316-5326, 2020	51	2020
Telescoping density-ratio estimation B Rhodes, K Xu, MU Gutmann Advances in neural information processing systems 33, 4905-4916, 2020	50	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	49	2015
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	47	2018
Efficient Bayesian experimental design for implicit models S Kleinegesse, MU Gutmann The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	45	2019
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	43	2013

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