Gutmann, Michael U.

26 publications

NeurIPS 2025 Neural Mutual Information Estimation with Vector Copulas Yanzhi Chen, Zijing Ou, Adrian Weller, Michael U. Gutmann

TMLR 2024 Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families Vaidotas Simkus, Michael U. Gutmann

TMLR 2023 Bayesian Optimization with Informative Covariance Afonso Eduardo, Michael U. Gutmann

TMLR 2023 Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling Vaidotas Simkus, Michael U. Gutmann

TMLR 2023 Estimating the Density Ratio Between Distributions with High Discrepancy Using Multinomial Logistic Regression Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann

ICML 2023 Is Learning Summary Statistics Necessary for Likelihood-Free Inference? Yanzhi Chen, Michael U. Gutmann, Adrian Weller

JMLR 2023 Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann

TMLR 2022 Enhanced Gradient-Based MCMC in Discrete Spaces Benjamin Rhodes, Michael U. Gutmann

NeurIPSW 2021 Bayesian Optimal Experimental Design for Simulator Models of Cognition Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Christopher G. Lucas

NeurIPS 2021 Implicit Deep Adaptive Design: Policy-Based Experimental Design Without Likelihoods Desi R Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

ICLR 2021 Neural Approximate Sufficient Statistics for Implicit Models Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

ICML 2020 Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation Steven Kleinegesse, Michael U. Gutmann

ICLR 2020 Generative Ratio Matching Networks Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

AISTATS 2020 Robust Optimisation Monte Carlo Borislav Ikonomov, Michael U. Gutmann

NeurIPS 2020 Telescoping Density-Ratio Estimation Benjamin Rhodes, Kai Xu, Michael U. Gutmann

AISTATS 2019 Adaptive Gaussian Copula ABC Yanzhi Chen, Michael U. Gutmann

AISTATS 2019 Efficient Bayesian Experimental Design for Implicit Models Steven Kleinegesse, Michael U. Gutmann

AISTATS 2019 Variational Noise-Contrastive Estimation Benjamin Rhodes, Michael U. Gutmann

ICML 2018 Conditional Noise-Contrastive Estimation of Unnormalised Models Ciwan Ceylan, Michael U. Gutmann

MLOSS 2018 ELFI: Engine for Likelihood-Free Inference Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski

CoRL 2017 Adaptable Pouring: Teaching Robots Not to Spill Using Fast but Approximate Fluid Simulation Tatiana Lopez-Guevara, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr

NeurIPS 2017 VEEGAN: Reducing Mode Collapse in GANs Using Implicit Variational Learning Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton

JMLR 2016 Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models Michael U. Gutmann, Jukka Corander

ECML-PKDD 2013 Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama

JMLR 2012 Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics Michael U. Gutmann, Aapo Hyvärinen

ACML 2012 Topographic Analysis of Correlated Components Hiroaki Sasaki, Michael U. Gutmann, Hayaru Shouno, Aapo Hyvärinen