Manuscripts

  1. Robust Optimisation Monte Carlo B. Ikonomov, and M. Gutmann arXiv:1904.00670 2019 [abs] [bib] [url]
  2. Parallel Gaussian process surrogate method to accelerate likelihood-free inference M. Järvenpää, M. Gutmann, A. Vehtari, and P. Marttinen arXiv:1905.01252 2019 [abs] [bib] [url]
  3. Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth-death models J. Lintusaari, P. Blomstedt, T. Sivula, M. Gutmann, S. Kaski, and J. Corander Wellcome Open Research 2019 [abs] [bib] [url]
  4. Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) T. Dinev, and M. Gutmann arXiv:1810.09899 2018 [abs] [bib] [url]
  5. Multiomic definition of generalizable endotypes in human acute pancreatitis L. Neyton, X. Zheng, C. Skouras, A. Doeschl-Wilson, M. Gutmann, C. Yau, C. Ponting, I. Uings, F. Rao, A. Nicolas, C. Marshall, L. Wilson, . APPreSci Consortium, J. Baillie, and D. Mole bioRxiv 539569 2018 [bib] [url]
  6. Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models A. Srivastava, K. Xu, M. Gutmann, and C. Sutton arXiv:1806.00101 2018 [abs] [bib] [url]
  7. Likelihood-Free Inference by Ratio Estimation O. Thomas, R. Dutta, J. Corander, S. Kaski, and M. Gutmann arXiv:1611.10242 2016 [abs] [bib] [url]

Refereed papers

  1. Adaptive Gaussian Copula ABC Y. Chen, and M. Gutmann In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 [abs] [bib] [url]
  2. Efficient Bayesian Experimental Design for Implicit Models S. Kleinegesse, and M. Gutmann In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 [abs] [bib] [url]
  3. Variational Noise-Contrastive Estimation B. Rhodes, and M. Gutmann In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 [abs] [bib] [url]
  4. Bayesian inference of atomistic structure in functional materials M. Todorović, M. Gutmann, J. Corander, and P. Rinke npj Computational Materials 2019 [pdf] [abs] [bib] [url]
  5. Weak Epistasis May Drive Adaptation in Recombining Bacteria B. Arnold, M. Gutmann, Y. Grad, S. Sheppard, J. Corander, M. Lipsitch, and W. Hanage Genetics 2018 [pdf] [abs] [bib] [url]
  6. Conditional Noise-Contrastive Estimation of Unnormalised Models C. Ceylan, and M. Gutmann In Proceedings of the 35th International Conference on Machine Learning (ICML) 2018 [pdf] [supp] [merged] [abs] [bib] [url]
  7. Likelihood-free inference via classification M. Gutmann, R. Dutta, S. Kaski, and J. Corander Statistics and Computing 2018 [pdf] [supp] [arxiv] [abs] [bib] [url] [slides] [slides]
  8. Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria M. Järvenpää, M. Gutmann, A. Vehtari, and P. Marttinen The Annals of Applied Statistics 2018 [pdf] [arxiv] [abs] [bib] [url]
  9. Efficient acquisition rules for model-based approximate Bayesian computation M. Järvenpää, M. Gutmann, A. Vehtari, and P. Marttinen Bayesian Analysis 2018 [arxiv] [abs] [bib] [url]
  10. ELFI: Engine for Likelihood-Free Inference J. Lintusaari, H. Vuollekoski, A. Kangasrääsiö, K. Skytén, M. Järvenpää, P. Marttinen, M. Gutmann, A. Vehtari, and J. Corander Journal of Machine Learning Research 2018 [pdf] [abs] [bib] [url]
  11. Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation T. Lopez-Guevara, N. Taylor, M. Gutmann, S. Ramamoorthy, and K. Subr In Proceedings of the 1st Annual Conference on Robot Learning (CoRL) 2017 [pdf] [abs] [bib] [url]
  12. Fundamentals and Recent Developments in Approximate Bayesian Computation J. Lintusaari, M. Gutmann, R. Dutta, S. Kaski, and J. Corander Systematic Biology 2017 [pdf] [abs] [bib] [url] [slides] [slides]
  13. Frequency-dependent selection in vaccine-associated pneumococcal population dynamics J. Corander, C. Fraser, M. Gutmann, B. Arnold, W. Hanage, S. Bentley, M. Lipsitch, and N. Croucher Nature Ecology & Evolution 2017 [abs] [bib] [url]
  14. Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen Neural Computation 2017 [pdf] [abs] [bib] [url]
  15. VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning A. Srivastava, A. Valkov, C. Russell, M. Gutmann, and C. Sutton In Advances in Neural Information Processing Systems 30 (NIPS) 2017 [pdf] [abs] [bib] [url]
  16. Bayesian inference of physiologically meaningful parameters from body sway measurements A. Tietäväinen, M. Gutmann, E. Keski-Vakkuri, J. Corander, and E. Haeggström Scientific Reports 2017 [pdf] [abs] [bib] [url]
  17. Bayesian optimization for likelihood-free inference of simulator-based statistical models M. Gutmann, and J. Corander Journal of Machine Learning Research 2016 [pdf] [arxiv] [abs] [bib] [url] [slides] [slides] [slides]
  18. On the identifiability of transmission dynamic models for infectious diseases J. Lintusaari, M. Gutmann, S. Kaski, and J. Corander Genetics 2016 [pdf] [preprint] [abs] [bib] [url]
  19. The impact of host metapopulation structure on the population genetics of colonizing bacteria E. Numminen, M. Gutmann, M. Shubin, P. Marttinen, G. Méric, W. Schaik, T. Coque, F. Baquero, R. Willems, S. Sheppard, E. Feil, W. Hanage, and J. Corander Journal of Theoretical Biology 2016 [pdf] [abs] [bib] [url]
  20. Recombination produces coherent bacterial species clusters in both core and accessory genomes P. Marttinen, N. Croucher, M. Gutmann, J. Corander, and W. Hanage Microbial Genomics 2015 [pdf] [supp] [abs] [bib] [url]
  21. Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images M. Gutmann, V. Laparra, A. Hyvärinen, and J. Malo PLOS ONE 2014 [pdf] [abs] [bib] [url]
  22. Direct learning of sparse changes in Markov networks by density ratio estimation S. Liu, J. Quinn, M. Gutmann, T. Suzuki, and M. Sugiyama Neural Computation 2014 [pdf] [abs] [bib] [url]
  23. Estimating dependency structures for non-Gaussian components with linear and energy correlations H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2014 [pdf] [supp] [abs] [bib] [url]
  24. A three-layer model of natural image statistics M. Gutmann, and A. Hyvärinen Journal of Physiology-Paris 2013 [pdf] [preprint] [abs] [bib] [url] [slides] [slides]
  25. Estimation of unnormalized statistical models without numerical integration M. Gutmann, and A. Hyvärinen In Proceedings of the Workshop on Information Theoretic Methods in Science and Engineering 2013 [pdf] [abs] [bib] [url]
  26. Direct learning of sparse changes in Markov networks by density ratio estimation S. Liu, J. Quinn, M. Gutmann, and M. Sugiyama In Machine Learning and Knowledge Discovery in Databases (ECML PKDD) 2013 [pdf] [abs] [bib] [url]
  27. Correlated topographic analysis: estimating an ordering of correlated components H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen Machine Learning 2013 [pdf] [abs] [bib] [url]
  28. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics M. Gutmann, and A. Hyvärinen Journal of Machine Learning Research 2012 [pdf] [abs] [bib] [url] [slides] [slides]
  29. Learning a selectivity–invariance–selectivity feature extraction architecture for images M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Pattern Recognition (ICPR) 2012 [pdf] [abs] [bib] [slides]
  30. Topographic analysis of correlated components H. Sasaki, M. Gutmann, H. Shouno, and A. Hyvärinen In JMLR: Workshop and Conference Proceedings 2012 [pdf] [abs] [bib] [url]
  31. Bregman divergence as general framework to estimate unnormalized statistical models M. Gutmann, and J. Hirayama In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 2011 [pdf] [abs] [bib] [url]
  32. Extracting coactivated features from multiple data sets M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Artificial Neural Networks (ICANN) 2011 [pdf] [supp] [abs] [bib] [url] [slides]
  33. Complex-valued independent component analysis of natural images V. Laparra, M. Gutmann, J. Malo, and A. Hyvärinen In Proceedings of the International Conference on Artificial Neural Networks (ICANN) 2011 [pdf] [abs] [bib] [url]
  34. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2010 [pdf] [abs] [bib] [url]
  35. A family of computationally efficient and simple estimators for unnormalized statistical models M. Pihlaja, M. Gutmann, and A. Hyvärinen In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 2010 [pdf] [abs] [bib]
  36. Learning features by contrasting natural images with noise M. Gutmann, and A. Hyvärinen In Proceedings of the International Conference on Artificial Neural Networks (ICANN) 2009 [pdf] [abs] [bib] [url] [slides]
  37. Learning reconstruction and prediction of natural stimuli by a population of spiking neurons M. Gutmann, and A. Hyvärinen In European Symposium on Artificial Neural Networks (ESANN) 2009 [pdf] [supp] [abs] [bib]
  38. Learning natural image structure with a horizonal product model U. Köster, J. Lindgren, M. Gutmann, and A. Hyvärinen In Proceedings on the International Conference on Independent Component Analysis and Signal Separation 2009 [pdf] [abs] [bib] [url]
  39. Toward data representation with spiking neurons M. Gutmann, and K. Aihara Artificial Life and Robotics 2008 [pdf] [preprint] [abs] [bib] [url]
  40. Learning encoding and decoding filters for data representation with a spiking neuron M. Gutmann, A. Hyvärinen, and K. Aihara In Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2008 [pdf] [abs] [bib] [url]
  41. Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2 A. Hyvärinen, M. Gutmann, and P. Hoyer BMC Neuroscience 2005 [pdf] [abs] [bib] [url]
  42. Statistical models of images and early vision A. Hyvärinen, P. Hoyer, J. Hurri, and M. Gutmann In Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR) 2005 [pdf] [abs] [bib] [url]