M. Gutmann

发表

Estimation of unnormalized statistical models without numerical integration

Aapo Hyvärinen, Michael U. Gutmann, A. Hyvärinen, 2013 .

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2010, AISTATS.

Approximate Bayesian Computation

M. Gutmann, Jessi Cisewski, Mark A. Beaumont, 2019, Annual Review of Statistics and Its Application.

Fundamentals and Recent Developments in Approximate Bayesian Computation

M. Gutmann, Jarno Lintusaari, Ritabrata Dutta, 2016, Systematic biology.

Statistical Inference of Intractable Generative Models via Classification

M. Gutmann, Ritabrata Dutta, Samuel Kaski, 2014 .

Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2012, J. Mach. Learn. Res..

Simultaneous Estimation of Nongaussian Components and Their Correlation Structure

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2015, Neural Computation.

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

A. Hyvärinen, M. Gutmann, Valero Laparra, 2014, PloS one.

Likelihood-free inference by penalised logistic regression

M. Gutmann, Ritabrata Dutta, Samuel Kaski, 2016 .

Learning Features by Contrasting Natural Images with Noise

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2009, ICANN.

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

Charles A. Sutton, Michael U. Gutmann, Chris Russell, 2017, NIPS.

Likelihood-Free Inference by Ratio Estimation

Jukka Corander, Owen Thomas, Samuel Kaski, 2016, Bayesian Analysis.

Frequency-dependent selection in vaccine-associated pneumococcal population dynamics

M. Gutmann, J. Corander, M. Lipsitch, 2017, Nature Ecology & Evolution.

Weak Epistasis May Drive Adaptation in Recombining Bacteria

M. Gutmann, J. Corander, M. Lipsitch, 2017, Genetics.

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

Masashi Sugiyama, Taiji Suzuki, John A. Quinn, 2013, Neural Computation.

Efficient Bayesian Inference of Atomistic Structure in Complex Functional Materials

M. Gutmann, J. Corander, P. Rinke, 2017 .

Likelihood-free inference via classification

Jukka Corander, Samuel Kaski, Michael U. Gutmann, 2014, Statistics and Computing.

Toward Data Representation with Formal Spiking Neurons

M. Gutmann, Michael U Gutmann, 2008 .

Parallel Gaussian process surrogate method to accelerate likelihood-free inference pdf

Aki Vehtari, Michael U. Gutmann, Pekka Marttinen, 2019, ArXiv.

Recombination produces coherent bacterial species clusters in both core and accessory genomes

M. Gutmann, J. Corander, W. Hanage, 2015, Microbial genomics.

Approximate Bayesian Computation

M. Gutmann, Michael U Gutmann, 2012 .

Statistical models of images and early vision

A. Hyvärinen, P. Hoyer, M. Gutmann, 2005 .

Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models

M. Gutmann, Jarno Lintusaari, Samuel Kaski, 2019, Wellcome Open Research.

Statistical applications of contrastive learning

M. Gutmann, S. Kleinegesse, Michael U Gutmann, 2022, Behaviormetrika.

The impact of host metapopulation structure on the population genetics of colonizing bacteria

M. Gutmann, J. Corander, W. Hanage, 2015, bioRxiv.

ELFI, a software package for likelihood-free inference

Aki Vehtari, Jukka Corander, Samuel Kaski, 2017, ICML 2017.

Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models

M. Gutmann, Charles Sutton, Kai Xu, 2018, ArXiv.

Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria

Aki Vehtari, Marko Jarvenpaa, Pekka Marttinen, 2016, The Annals of Applied Statistics.

Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions

M. Gutmann, K. Subr, S. Ramamoorthy, 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2010 .

Proc. Workshop on Information Theoretic Methods in Science and Engineering (WITMSE2013)

Aapo Hyvärinen, Michael U. Gutmann, A. Hyvärinen, 2013 .