Source on GitHub

Publications

  1. Manuscripts
  2. Refereed papers
  3. Workshop and other papers

Google Scholar Semantic Scholar UoE Paper Repo

Manuscripts

Simulation-based Bayesian inference under model misspecification
R. P. Kelly, D. J. Warne, D. T. Frazier, D. J. Nott, M. U. Gutmann, C. Drovandi
arXiv:2503.12315, 2025
[url] [arxiv]

Risk-averse optimization of genetic circuits under uncertainty
M. Kobiela, D. A. Oyarzun, M. U. Gutmann
bioRxiv, 2024
[url] [arxiv]

Refereed papers

Neural Mutual Information Estimation with Vector Copulas
Y. Chen, Z. Ou, A. Weller, M. U. Gutmann
The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
[url] [arxiv]

Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
V. Simkus, M. U. Gutmann
Transactions on Machine Learning Research (TMLR), 2024
[url] [arxiv]

Designing optimal behavioral experiments using machine learning
S. Valentin, S. Kleinegesse, N. R. Bramley, P. Seriès, M. U. Gutmann, C. G. Lucas
eLife, 2024
[url] [arxiv]

Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
V. Simkus, M. U. Gutmann
Transactions on Machine Learning Research, 2023
[url] [arxiv]

An Extendable Python Implementation of Robust Optimisation Monte Carlo
V. Gkolemis, M. Gutmann, H. Pesonen
Journal of Statistical Software, 2023
[url] [arxiv]

Is Learning Summary Statistics Necessary for Likelihood-free Inference?
Y. Chen, M. U. Gutmann, A. Weller
Proceedings of the 40th International Conference on Machine Learning (ICML), 2023
[url]

Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
V. Simkus, B. Rhodes, M. U. Gutmann
Journal of Machine Learning Research, 2023
[url] [arxiv]

Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
A. Srivastava, S. Han, K. Xu, B. Rhodes, M. U. Gutmann
Transactions on Machine Learning Research, 2023
[url] [arxiv]

Bayesian Optimization with Informative Covariance
A. Eduardo, M. U. Gutmann
Transactions on Machine Learning Research, 2023
[url] [arxiv]

Enhanced gradient-based MCMC in discrete spaces
B. Rhodes, M. U. Gutmann
Transactions on Machine Learning Research, 2022
[url] [arxiv]

Inference and uncertainty quantification of stochastic gene expression via synthetic models
K. Öcal, M. U. Gutmann, G. Sanguinetti, R. Grima
Journal of The Royal Society Interface, 2022
[url] [arxiv]

Systematic comparison of ranking aggregation methods for gene lists in experimental results
B. Wang, A. Law, T. Regan, N. Parkinson, J. Cole, C. D. Russell, D. H. Dockrell, M. U. Gutmann, J. K. Baillie
Bioinformatics, 2022
[url] [arxiv]

Statistical applications of contrastive learning
M. U. Gutmann, S. Kleinegesse, B. Rhodes
Behaviormetrika, 2022
[url] [arxiv]

Bayesian Optimal Experimental Design for Simulator Models of Cognition
S. Valentin, S. Kleinegesse, N. R. Bramley, M. U. Gutmann, C. G. Lucas
NeurIPS 2021 Workshop "AI for Science", 2021
[url] [arxiv]

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
D. R. Ivanova, A. Foster, S. Kleinegesse, M. U. Gutmann, T. Rainforth
Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeuRIPS 2021), 2021
[url] [arxiv]

Neural Approximate Sufficient Statistics for Implicit Models
Y. Chen, D. Zhang, M. U. Gutmann, A. Courville, Z. Zhu
International Conference on Learning Representations (ICLR), 2021
[url] [arxiv]

Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
S. Kleinegesse, C. Drovandi, M. U. Gutmann
Bayesian Analysis, 2021
[url] [arxiv]

Adaptive Approximate Bayesian Computation Tolerance Selection
U. Simola, J. Cisewski-Kehe, M. U. Gutmann, J. Corander
Bayesian Analysis, 2021
[url] [arxiv]

Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
M. Järvenpää, M. U. Gutmann, A. Vehtari, P. Marttinen
Bayesian Analysis, 2021
[url] [arxiv]

Telescoping Density-Ratio Estimation
B. Rhodes, K. Xu, M. U. Gutmann
Advances in Neural Information Processing Systems 34 (NeurIPS 2020), 2020
[url] [arxiv]

Likelihood-Free Inference by Ratio Estimation
O. Thomas, R. Dutta, J. Corander, S. Kaski, M. U. Gutmann
Bayesian Analysis, 2020
[url] [arxiv]

Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions
T. Lopez Guevara, R. Pucci, N. Taylor, M. U. Gutmann, R. Ramamoorthy, K. Subr
Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), 2020
[url]

Robust Optimisation Monte Carlo
B. Ikonomov, M. U. Gutmann
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
[url] [arxiv]

Molecular Patterns in Acute Pancreatitis Reflect Generalizable Endotypes of the Host Response to Systemic Injury in Humans
L. P. Neyton, X. Zheng, C. Skouras, A. Doeschl-Wilson, M. U. Gutmann, I. Uings, F. V. Rao, A. Nicolas, C. Marshall, L. Wilson, J. K. Baillie, D. J. Mole
Annals of Surgery, 2020
[url] [arxiv]

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
S. Kleinegesse, M. U. Gutmann
Proceedings of the 37th International Conference on Machine Learning (ICML), 2020
[url] [arxiv]

Generative Ratio Matching Networks
A. Srivastava, K. Xu, M. U. Gutmann, C. Sutton
Proceedings of the International Conference on Learning Representations (ICLR), 2020
[url] [arxiv]

Genome-wide CRISPR screen Identifies Host Dependency Factors for Influenza A Virus Infection
B. Li, S. M. Clohisey, B. S. Chia, B. Wang, A. Cui, T. Eisenhaure, L. D. Schweitzer, P. Hoover, N. J. Parkinson, A. Nachshon, N. Smith, T. Regan, D. Farr, M. U. Gutmann, S. I. Bukhari, A. Law, M. Sangesland, I. Gat-viks, P. Digard, S. Vasudevan, D. Lingwood, D. H. Dockrell, J. G. Doench, J. K. Baillie, N. Hacohen
Nature Communications, 2020
[url]

Efficient Bayesian Experimental Design for Implicit Models
S. Kleinegesse, M. U. Gutmann
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
[url] [arxiv]

Bayesian inference of atomistic structure in functional materials
M. Todorović, M. U. Gutmann, J. Corander, P. Rinke
npj Computational Materials, 2019
[url] [arxiv]

Variational Noise-Contrastive Estimation
B. Rhodes, M. U. Gutmann
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
[url] [arxiv]

Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth-death models
J. Lintusaari, P. Blomstedt, B. Rose, T. Sivula, M. U. Gutmann, S. Kaski, J. Corander
Wellcome Open Research, 2019
[url] [arxiv]

Efficient acquisition rules for model-based approximate Bayesian computation
M. Järvenpää, M. U. Gutmann, A. Vehtari, P. Marttinen
Bayesian Analysis, 2019
[url] [arxiv]

Adaptive Gaussian Copula ABC
Y. Chen, M. U. Gutmann
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
[url] [arxiv]

ELFI: Engine for Likelihood-Free Inference
J. Lintusaari, H. Vuollekoski, A. Kangasrääsiö, K. Skytén, M. Järvenpää, P. Marttinen, M. U. Gutmann, A. Vehtari, J. Corander
Journal of Machine Learning Research, 2018
[url] [arxiv]

Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
M. Järvenpää, M. U. Gutmann, A. Vehtari, P. Marttinen
The Annals of Applied Statistics, 2018
[url] [arxiv]

Likelihood-free inference via classification
M. U. Gutmann, R. Dutta, S. Kaski, J. Corander
Statistics and Computing, 2018
[url] [arxiv]

Conditional Noise-Contrastive Estimation of Unnormalised Models
C. Ceylan, M. U. Gutmann
Proceedings of the 35th International Conference on Machine Learning (ICML), 2018
[url] [arxiv]

Weak Epistasis May Drive Adaptation in Recombining Bacteria
B. Arnold, M. U. Gutmann, Y. Grad, S. Sheppard, J. Corander, M. Lipsitch, W. Hanage
Genetics, 2018
[url] [arxiv]

Bayesian inference of physiologically meaningful parameters from body sway measurements
A. Tietäväinen, M. U. Gutmann, E. Keski-Vakkuri, J. Corander, E. Haeggström
Scientific Reports, 2017
[url]

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
A. Srivastava, A. Valkov, C. Russell, M. U. Gutmann, C. Sutton
Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017
[url] [arxiv]

Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
H. Sasaki, M. U. Gutmann, H. Shouno, A. Hyvärinen
Neural Computation, 2017
[url] [arxiv]

Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation
T. Lopez-Guevara, N. Taylor, M. U. Gutmann, S. Ramamoorthy, K. Subr
Proceedings of the 1st Annual Conference on Robot Learning (CoRL), 2017
[url]

Fundamentals and Recent Developments in Approximate Bayesian Computation
J. Lintusaari, M. U. Gutmann, R. Dutta, S. Kaski, J. Corander
Systematic Biology, 2017
[url]

Frequency-dependent selection in vaccine-associated pneumococcal population dynamics
J. Corander, C. Fraser, M. U. Gutmann, B. Arnold, W. Hanage, S. Bentley, M. Lipsitch, N. Croucher
Nature Ecology & Evolution, 2017
[url]

The impact of host metapopulation structure on the population genetics of colonizing bacteria
E. Numminen, M. U. Gutmann, M. Shubin, P. Marttinen, G. Méric, W. Schaik, T. Coque, F. Baquero, R. Willems, S. Sheppard, E. Feil, W. Hanage, J. Corander
Journal of Theoretical Biology, 2016
[url] [arxiv]

On the identifiability of transmission dynamic models for infectious diseases
J. Lintusaari, M. U. Gutmann, S. Kaski, J. Corander
Genetics, 2016
[url] [arxiv]

Bayesian optimization for likelihood-free inference of simulator-based statistical models
M. U. Gutmann, J. Corander
Journal of Machine Learning Research, 2016
[url] [arxiv]

Recombination produces coherent bacterial species clusters in both core and accessory genomes
P. Marttinen, N. Croucher, M. U. Gutmann, J. Corander, W. Hanage
Microbial Genomics, 2015
[url]

Estimating dependency structures for non-Gaussian components with linear and energy correlations
H. Sasaki, M. U. Gutmann, H. Shouno, A. Hyvärinen
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
[url]

Direct learning of sparse changes in Markov networks by density ratio estimation
S. Liu, J. Quinn, M. U. Gutmann, T. Suzuki, M. Sugiyama
Neural Computation, 2014
[url] [arxiv]

Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural images
M. U. Gutmann, V. Laparra, A. Hyvärinen, J. Malo
PLOS ONE, 2014
[url]

Correlated topographic analysis: estimating an ordering of correlated components
H. Sasaki, M. U. Gutmann, H. Shouno, A. Hyvärinen
Machine Learning, 2013
[url]

Direct learning of sparse changes in Markov networks by density ratio estimation
S. Liu, J. Quinn, M. U. Gutmann, M. Sugiyama
Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2013
[url]

Estimation of unnormalized statistical models without numerical integration
M. U. Gutmann, A. Hyvärinen
Proceedings of the Workshop on Information Theoretic Methods in Science and Engineering, 2013
[url]

A three-layer model of natural image statistics
M. U. Gutmann, A. Hyvärinen
Journal of Physiology-Paris, 2013
[url]

Topographic analysis of correlated components
H. Sasaki, M. U. Gutmann, H. Shouno, A. Hyvärinen
JMLR: Workshop and Conference Proceedings, 2012
[url]

Learning a selectivity--invariance--selectivity feature extraction architecture for images
M. U. Gutmann, A. Hyvärinen
Proceedings of the International Conference on Pattern Recognition (ICPR), 2012
[url]

Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
M. U. Gutmann, A. Hyvärinen
Journal of Machine Learning Research, 2012
[url]

Complex-valued independent component analysis of natural images
V. Laparra, M. U. Gutmann, J. Malo, A. Hyvärinen
Proceedings of the International Conference on Artificial Neural Networks (ICANN), 2011
[url]

Extracting coactivated features from multiple data sets
M. U. Gutmann, A. Hyvärinen
Proceedings of the International Conference on Artificial Neural Networks (ICANN), 2011
[url]

Bregman divergence as general framework to estimate unnormalized statistical models
M. U. Gutmann, J. Hirayama
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2011
[url] [arxiv]

A family of computationally efficient and simple estimators for unnormalized statistical models
M. Pihlaja, M. U. Gutmann, A. Hyvärinen
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2010
[url] [arxiv]

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M. U. Gutmann, A. Hyvärinen
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2010
[url]

Learning natural image structure with a horizonal product model
U. Köster, J. Lindgren, M. U. Gutmann, A. Hyvärinen
Proceedings on the International Conference on Independent Component Analysis and Signal Separation, 2009
[url]

Learning reconstruction and prediction of natural stimuli by a population of spiking neurons
M. U. Gutmann, A. Hyvärinen
European Symposium on Artificial Neural Networks (ESANN), 2009
[url]

Learning features by contrasting natural images with noise
M. U. Gutmann, A. Hyvärinen
Proceedings of the International Conference on Artificial Neural Networks (ICANN), 2009
[url]

Learning encoding and decoding filters for data representation with a spiking neuron
M. Gutmann, A. Hyvärinen, K. Aihara
Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2008
[url]

Toward data representation with spiking neurons
M. U. Gutmann, K. Aihara
Artificial Life and Robotics, 2008
[url]

Statistical models of images and early vision
A. Hyvärinen, P. Hoyer, J. Hurri, M. Gutmann
Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR), 2005
[url]

Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2
A. Hyvärinen, M. Gutmann, P. Hoyer
BMC Neuroscience, 2005
[url]

Workshop and other papers

CFMI: Flow Matching for Missing Data Imputation
V. Simkus, M. U. Gutmann
arXiv:2506.09258, 2025
[url] [arxiv]

Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
S. Kleinegesse, M. U. Gutmann
arXiv:2105.04379, 2021
[url] [arxiv]

Bayesian Optimal Experimental Design for Simulator Models of Cognition
S. Valentin, S. Kleinegesse, . R. Bramley, . U. Gutmann, . G. Lucas
NeurIPS 2021 Workshop "AI for Science", 2021
[url] [arxiv]

Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)
T. Dinev, M. Gutmann
arXiv:1810.09899, 2018
[url] [arxiv]

To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions
T. Lopez Guevara, R. Pucci, N. Taylor, M. U. Gutmann, S. Ramamoorthy, K. Subr
Workshop on Learning and Inference in Robotics: Integrating Structure, Priors and Models, 2018
[url] [arxiv]

Classification and Bayesian Optimization for Likelihood-Free Inference
M. Gutmann, J. Corander, R. Dutta, S. Kaski
arXiv:1502.05503, 2015
[url] [arxiv]

Learning topographic representations for linearly correlated components
H. Sasaki, M. Gutmann, H. Shouno, A. Hyvärinen
Workshop on Deep Learning and Unsupervised Feature Learning, NIPS, 2011
[url]

Learning spike-timings based representations of sensory stimuli with leaky integrate-and-fire neurons
M. Gutmann, A. Hyvärinen
BMC Neuroscience, 2009
[url]

Unsupervised learning by discriminating data from artificial noise
M. Gutmann, A. Hyvärinen
NIPS Workshop on Generative Discriminative Learning Interface, 2009
[url]

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