Michael U Gutmann

发表

On the Identifiability of Transmission Dynamic Models for Infectious Diseases

Jukka Corander, Samuel Kaski, Jarno Lintusaari, 2015, Genetics.

Fundamentals and Recent Developments in Approximate Bayesian Computation.

Jukka Corander, Samuel Kaski, Jarno Lintusaari, 2016, Systematic biology.

Variational Gibbs inference for statistical model estimation from incomplete data

Michael U Gutmann, Benjamin Rhodes, Vaidotas Šimkus, 2021, 2111.13180.

ELFI, a software package for likelihood-free inference

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

Statistical models of images and early vision

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

Pattern Recognition (ICPR), 2012 21st International Conference on

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2012 .

Proc. Int. Joint Conference on Neural Networks (IJCNN)

Kazuyuki Aihara, Aapo Hyvärinen, Michael Gutmann, 2008 .

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

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

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

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

Enhanced gradient-based MCMC in discrete spaces

Michael U Gutmann, Benjamin Rhodes, 2022, Trans. Mach. Learn. Res..

Weak Epistasis May Drive Adaptation in Recombining Bacteria

J. Corander, M. Lipsitch, W. Hanage, 2017, Genetics.

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

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

Proc. Asian Conference on Machine Learning (ACML)

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2012 .

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

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

Edinburgh Explorer Likelihood-free inference by ratio estimation

Ritabrata Dutta, Samuel Kaski, J. Corander, 2022 .

Bayesian Optimization with Informative Covariance

Michael U Gutmann, Afonso Eduardo, 2022, 2208.02704.

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

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

Proc. Conf. on Uncertainty in Artificial Intelligence (UAI)

Aapo Hyvärinen, Michael U. Gutmann, Miika Pihlaja, 2010 .

Learning Natural Image Structure with a Horizontal Product Model

Aapo Hyvärinen, Michael Gutmann, Urs Köster, 2009, ICA.

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods pdf

Michael U. Gutmann, Tom Rainforth, Steven Kleinegesse, 2021, NeurIPS.

Neural Approximate Sufficient Statistics for Implicit Models pdf

Zhanxing Zhu, Aaron C. Courville, Aaron Courville, 2020, ICLR.

Approximate Bayesian Computation

M. Gutmann, Michael U Gutmann, 2012 .

Likelihood-Free Inference by Ratio Estimation

Jukka Corander, Samuel Kaski, Michael U. Gutmann, 2016, Bayesian Analysis.

Adaptive Gaussian Copula ABC pdf

Michael U. Gutmann, Yanzhi Chen, Michael U Gutmann, 2019, AISTATS.

Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation

Aki Vehtari, Michael U. Gutmann, Marko Jarvenpaa, 2017, Bayesian Analysis.

Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) pdf

Michael U. Gutmann, Traiko Dinev, Traiko Dinev, 2018, ArXiv.

Weak Epistasis May Drive Adaptation in Recombining Bacteria

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

Likelihood-free inference via classification

Ritabrata Dutta, Jukka Corander, Samuel Kaski, 2014, Stat. Comput..

Frequency-dependent selection in vaccine-associated pneumococcal population dynamics

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

Fundamentals and Recent Developments in Approximate Bayesian Computation

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

Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

Michael U. Gutmann, Jukka Corander, J. Corander, 2015, J. Mach. Learn. Res..

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

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

Bregman divergence as general framework to estimate unnormalized statistical models

Junichiro Hirayama, Michael Gutmann, Michael U Gutmann, 2011, UAI.

A Family of Computationally E cient and Simple Estimators for Unnormalized Statistical Models

Aapo Hyvärinen, Michael Gutmann, Miika Pihlaja, 2010, UAI.

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2013, Machine Learning.

Learning Topographic Representations for Linearly Correlated Components

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2011 .

Statistical models of images and early vision

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2005 .

Simultaneous Estimation of Nongaussian Components and Their Correlation Structure

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

Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2014, AISTATS.

Topographic Analysis of Correlated Components

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2012, ACML.

Frequency-dependent selection in vaccine-associated pneumococcal population dynamics

J. Corander, M. Lipsitch, C. Fraser, 2017, Nature Ecology & Evolution.

Adaptive Approximate Bayesian Computation Tolerance Selection

Jukka Corander, Michael U. Gutmann, Umberto Simola, 2019, Bayesian Analysis.

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

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

Inference and uncertainty quantification of stochastic gene expression via synthetic models

G. Sanguinetti, R. Grima, Kaan Öcal, 2022, bioRxiv.

Bayesian inference of physiologically meaningful parameters from body sway measurements

A Tietäväinen, M U Gutmann, E Keski-Vakkuri, 2017, Scientific Reports.

17th European Symposium on Artificial Neural Networks (ESANN)

Aapo Hyvärinen, Michael Gutmann, Michael U Gutmann, 2009 .

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

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

Telescoping Density-Ratio Estimation pdf

Kai Xu, Michael U. Gutmann, Benjamin Rhodes, 2020, NeurIPS.

Bayesian inference of atomistic structure in functional materials

Jukka Corander, Patrick Rinke, Milica Todorović, 2017, npj Computational Materials.

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

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

Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation

Michael U. Gutmann, Kartic Subr, Nick K. Taylor, 2017, CoRL.

Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds pdf

Michael U. Gutmann, Steven Kleinegesse, S. Kleinegesse, 2021, ArXiv.

ELFI: Engine for Likelihood Free Inference

Aki Vehtari, Jukka Corander, Samuel Kaski, 2016, J. Mach. Learn. Res..

Sequential Bayesian Experimental Design for Implicit Models via Mutual Information pdf

Michael U. Gutmann, Steven Kleinegesse, Christopher Drovandi, 2020, ArXiv.

Efficient Bayesian Experimental Design for Implicit Models

Michael U. Gutmann, Steven Kleinegesse, S. Kleinegesse, 2018, AISTATS.

Learning Features by Contrasting Natural Images with Noise

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

Variational Gibbs inference for statistical model estimation from incomplete data

Michael U Gutmann, Benjamin Rhodes, Vaidotas Šimkus, 2021, 2111.13180.

Likelihood-free inference by penalised logistic regression

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

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

S. Kleinegesse, Michael U Gutmann, Steven Kleinegesse, 2020, ICML.

A three-layer model of natural image statistics

Aapo Hyvärinen, Michael U. Gutmann, Michael U Gutmann, 2013, Journal of Physiology-Paris.

Parallel Gaussian Process Surrogate Bayesian Inference with Noisy Likelihood Evaluations

Aki Vehtari, Marko Jarvenpaa, Pekka Marttinen, 2019, Bayesian Analysis.

Toward data representation with spiking neurons

Kazuyuki Aihara, Michael Gutmann, K. Aihara, 2008, Artificial Life and Robotics.

Toward Data Representation with Formal Spiking Neurons

M. Gutmann, Michael U Gutmann, 2008 .

Conditional Noise-Contrastive Estimation of Unnormalised Models

Michael U. Gutmann, Ciwan Ceylan, Michael U Gutmann, 2018, ICML.

Variational Noise-Contrastive Estimation

Michael U. Gutmann, Benjamin Rhodes, Michael U Gutmann, 2018, AISTATS.

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

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

Statistical applications of contrastive learning

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

Designing optimal behavioral experiments using machine learning

Neil R. Bramley, P. Seriès, S. Kleinegesse, 2023, eLife.

Likelihood-free inference via classification

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

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

Aki Vehtari, P. Marttinen, Michael U Gutmann, 2016, The Annals of Applied Statistics.

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

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

Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2

P. Hoyer, Michael U Gutmann, Aapo Hyvärinen, 2005, BMC Neuroscience.

An Extendable Python Implementation of Robust Optimisation Monte Carlo

Michael U Gutmann, Vasilis Gkolemis, Henri Pesonen, 2023, ArXiv.

Simultaneous Estimation of Nongaussian Components and Their Correlation Structure

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

Extending the statistical software package Engine for Likelihood-Free Inference pdf

Michael Gutmann, Vasileios Gkolemis, Michael U Gutmann, 2020, ArXiv.

Robust Optimisation Monte Carlo pdf

Michael U. Gutmann, Borislav Ikonomov, Michael U Gutmann, 2019, AISTATS.

Variational Gibbs inference for statistical model estimation from incomplete data

Michael U Gutmann, Benjamin Rhodes, Vaidotas Šimkus, 2021, J. Mach. Learn. Res..

Telescoping Density-Ratio Estimation pdf

Michael U. Gutmann, Kai Xu, Benjamin Rhodes, 2020, NeurIPS.

Bayesian Optimization with Informative Covariance

Michael U Gutmann, Afonso Eduardo, 2022, Trans. Mach. Learn. Res..

Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation

Aki Vehtari, P. Marttinen, Michael U Gutmann, 2017, Bayesian Analysis.

Efficient acquisition rules for model-based approximate Bayesian computation

Marttinen, Aki Vehtari, M. Järvenpää, 2018 .

Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling

Michael U Gutmann, Vaidotas Šimkus, 2023, Trans. Mach. Learn. Res..