Michael U. Gutmann

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 51

Top Topics

Mutual Information

Incomplete Data

Experimental Design

Statistical Model

Top Venues

Trans. Mach. Learn. Res.

J. Mach. Learn. Res.

Publications

Vaidotas Simkus, Michael U. Gutmann
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. CoRR (2024)
Vaidotas Simkus, Michael U. Gutmann
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. Trans. Mach. Learn. Res. 2024 (2024)
Vaidotas Simkus, Michael U. Gutmann
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling. Trans. Mach. Learn. Res. 2023 (2023)
Afonso Eduardo, Michael U. Gutmann
Bayesian Optimization with Informative Covariance. Trans. Mach. Learn. Res. 2023 (2023)
Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. CoRR (2023)
Vaidotas Simkus, Michael U. Gutmann
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling. CoRR (2023)
Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Peggy Seriès, Michael U. Gutmann, Christopher G. Lucas
Designing Optimal Behavioral Experiments Using Machine Learning. CoRR (2023)
Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data. J. Mach. Learn. Res. 24 (2023)
Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. Trans. Mach. Learn. Res. 2023 (2023)
Yanzhi Chen, Michael U. Gutmann, Adrian Weller
Is Learning Summary Statistics Necessary for Likelihood-free Inference? ICML (2023)
Michael U. Gutmann, Steven Kleinegesse, Benjamin Rhodes
Statistical applications of contrastive learning. CoRR (2022)
Afonso Eduardo, Michael U. Gutmann
Bayesian Optimization with Informative Covariance. CoRR (2022)
Bo Wang, Andy Law, Tim Regan, Nicholas Parkinson, Joby Cole, Clark D. Russell, David H. Dockrell, Michael U. Gutmann, J. Kenneth Baillie
Systematic comparison of ranking aggregation methods for gene lists in experimental results. Bioinform. 38 (21) (2022)
Benjamin Rhodes, Michael U. Gutmann
Enhanced gradient-based MCMC in discrete spaces. CoRR (2022)
Benjamin Rhodes, Michael U. Gutmann
Enhanced gradient-based MCMC in discrete spaces. Trans. Mach. Learn. Res. 2022 (2022)
Michael U. Gutmann
Pen and Paper Exercises in Machine Learning. CoRR (2022)
Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. NeurIPS (2021)
Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
Neural Approximate Sufficient Statistics for Implicit Models. ICLR (2021)
Steven Kleinegesse, Michael U. Gutmann
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds. CoRR (2021)
Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Chris Lucas
Bayesian Experimental Design for Intractable Models of Cognition. CogSci (2021)
Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
Variational Gibbs inference for statistical model estimation from incomplete data. CoRR (2021)
Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR (2021)
Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Christopher G. Lucas
Bayesian Optimal Experimental Design for Simulator Models of Cognition. CoRR (2021)
Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Telescoping Density-Ratio Estimation. NeurIPS (2020)
Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr
Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions. IROS (2020)
Steven Kleinegesse, Christopher C. Drovandi, Michael U. Gutmann
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information. CoRR (2020)
Steven Kleinegesse, Michael U. Gutmann
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. CoRR (2020)
Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Telescoping Density-Ratio Estimation. CoRR (2020)
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
Generative Ratio Matching Networks. ICLR (2020)
Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
Neural Approximate Sufficient Statistics for Implicit Models. CoRR (2020)
Borislav Ikonomov, Michael U. Gutmann
Robust Optimisation Monte Carlo. AISTATS (2020)
Steven Kleinegesse, Michael U. Gutmann
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. ICML (2020)
Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen
Parallel Gaussian process surrogate method to accelerate likelihood-free inference. CoRR (2019)
Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr
To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions. CoRR (2019)
Borislav Ikonomov, Michael U. Gutmann
Robust Optimisation Monte Carlo. CoRR (2019)
Yanzhi Chen, Michael U. Gutmann
Adaptive Gaussian Copula ABC. AISTATS (2019)
Yanzhi Chen, Michael U. Gutmann
Adaptive Gaussian Copula ABC. CoRR (2019)
Benjamin Rhodes, Michael U. Gutmann
Variational Noise-Contrastive Estimation. AISTATS (2019)
Steven Kleinegesse, Michael U. Gutmann
Efficient Bayesian Experimental Design for Implicit Models. AISTATS (2019)
Ciwan Ceylan, Michael U. Gutmann
Conditional Noise-Contrastive Estimation of Unnormalised Models. CoRR (2018)
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
ELFI: Engine for Likelihood-Free Inference. J. Mach. Learn. Res. 19 (2018)
Traiko Dinev, Michael U. Gutmann
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE). CoRR (2018)
Benjamin Rhodes, Michael U. Gutmann
Variational Noise-Contrastive Estimation. CoRR (2018)
Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander
Likelihood-free inference via classification. Stat. Comput. 28 (2) (2018)
Steven Kleinegesse, Michael U. Gutmann
Efficient Bayesian Experimental Design for Implicit Models. CoRR (2018)
Ciwan Ceylan, Michael U. Gutmann
Conditional Noise-Contrastive Estimation of Unnormalised Models. ICML (2018)
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR (2018)
Tatiana Lopez-Guevara, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr
Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation. CoRL (2017)
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
ELFI: Engine for Likelihood Free Inference. CoRR (2017)
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS (2017)
Michael U. Gutmann, Jukka Corander
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models. J. Mach. Learn. Res. 17 (2016)
Song Liu, John A. Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi Sugiyama
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. Neural Comput. 26 (6) (2014)
Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. ECML/PKDD (2) (2013)