Michael U. Gutmann

According to our database, Michael U. Gutmann authored at least 38 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families.

Vaidotas Simkus, Michael U. Gutmann

Trans. Mach. Learn. Res., 2024

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

Trans. Mach. Learn. Res., 2023

Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling.

Vaidotas Simkus, Michael U. Gutmann

Trans. Mach. Learn. Res., 2023

Bayesian Optimization with Informative Covariance.

Afonso Eduardo, Michael U. Gutmann

Trans. Mach. Learn. Res., 2023

Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data.

Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann

J. Mach. Learn. Res., 2023

Designing Optimal Behavioral Experiments Using Machine Learning.

Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Peggy Seriès, Michael U. Gutmann, Christopher G. Lucas

CoRR, 2023

Is Learning Summary Statistics Necessary for Likelihood-free Inference?

Yanzhi Chen, Michael U. Gutmann, Adrian Weller

Proceedings of the International Conference on Machine Learning, 2023

2022

Systematic comparison of ranking aggregation methods for gene lists in experimental results.

Bo Wang, Andy Law, Tim Regan, Nicholas Parkinson, Joby Cole, Clark D. Russell, David H. Dockrell, Michael U. Gutmann, J. Kenneth Baillie

Bioinform., October, 2022

Enhanced gradient-based MCMC in discrete spaces.

Benjamin Rhodes, Michael U. Gutmann

Trans. Mach. Learn. Res., 2022

Pen and Paper Exercises in Machine Learning.

Michael U. Gutmann

CoRR, 2022

Statistical applications of contrastive learning.

Michael U. Gutmann, Steven Kleinegesse, Benjamin Rhodes

CoRR, 2022

2021

Bayesian Optimal Experimental Design for Simulator Models of Cognition.

Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Christopher G. Lucas

CoRR, 2021

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

Steven Kleinegesse, Michael U. Gutmann

CoRR, 2021

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

Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Approximate Sufficient Statistics for Implicit Models.

Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu

Proceedings of the 9th International Conference on Learning Representations, 2021

Bayesian Experimental Design for Intractable Models of Cognition.

Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Chris Lucas

Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021

2020

Sequential Bayesian Experimental Design for Implicit Models via Mutual Information.

Steven Kleinegesse, Christopher C. Drovandi, Michael U. Gutmann

CoRR, 2020

Telescoping Density-Ratio Estimation.

Benjamin Rhodes, Kai Xu, Michael U. Gutmann

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions.

Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation.

Steven Kleinegesse, Michael U. Gutmann

Proceedings of the 37th International Conference on Machine Learning, 2020

Generative Ratio Matching Networks.

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

Proceedings of the 8th International Conference on Learning Representations, 2020

Robust Optimisation Monte Carlo.

Borislav Ikonomov, Michael U. Gutmann

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

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

Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen

CoRR, 2019

To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions.

Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr

CoRR, 2019

Variational Noise-Contrastive Estimation.

Benjamin Rhodes, Michael U. Gutmann

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Efficient Bayesian Experimental Design for Implicit Models.

Steven Kleinegesse, Michael U. Gutmann

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Adaptive Gaussian Copula ABC.

Yanzhi Chen, Michael U. Gutmann

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Likelihood-free inference via classification.

Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander

Stat. Comput., 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

J. Mach. Learn. Res., 2018

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

Traiko Dinev, Michael U. Gutmann

CoRR, 2018

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

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

CoRR, 2018

Conditional Noise-Contrastive Estimation of Unnormalised Models.

Ciwan Ceylan, Michael U. Gutmann

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

ELFI: Engine for Likelihood Free Inference.

Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski

CoRR, 2017

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning.

Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 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

Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017

2016

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

Michael U. Gutmann, Jukka Corander

J. Mach. Learn. Res., 2016

2014

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

Song Liu, John A. Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi Sugiyama

Neural Comput., 2014

2013

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

Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013