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

Refereed papers

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

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]

CC BY-SA 4.0 Michael U. Gutmann. Last modified: November 15, 2024. Accessibility. Website built with Franklin.jl and the Julia programming language.

Publications

Manuscripts

Refereed papers

Workshop and other papers