Michael U. Gutmann

发表

The 1st Annual Conference on Robot Learning (CoRL 2017)

Nicholas Kenelm Taylor, Tatiana Lopez Guevara, Michael U. Gutmann, 2017 .

Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation

Aki Vehtari, Michael U. Gutmann, Marko Jarvenpaa, 2017, Bayesian Analysis.

Telescoping Density-Ratio Estimation pdf

Kai Xu, Michael U. Gutmann, Benjamin Rhodes, 2020, NeurIPS.

Sequential Bayesian Experimental Design for Implicit Models via Mutual Information pdf

Michael U. Gutmann, Steven Kleinegesse, Christopher Drovandi, 2020, ArXiv.

Conditional Noise-Contrastive Estimation of Unnormalised Models

Michael U. Gutmann, Ciwan Ceylan, Michael U Gutmann, 2018, ICML.

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

Masashi Sugiyama, Taiji Suzuki, John A. Quinn, 2013, Neural Computation.

Proc. Asian Conference on Machine Learning (ACML)

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2012 .

Variational Noise-Contrastive Estimation

Michael U. Gutmann, Benjamin Rhodes, Michael U Gutmann, 2018, AISTATS.

Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation

Michael U. Gutmann, Kartic Subr, Nick K. Taylor, 2017, CoRL.

Learning Topographic Representations for Linearly Correlated Components

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2011 .

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

Masashi Sugiyama, John A. Quinn, Michael U. Gutmann, 2013, ECML/PKDD.

Adaptive Gaussian Copula ABC pdf

Michael U. Gutmann, Yanzhi Chen, Michael U Gutmann, 2019, AISTATS.

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

Michael U. Gutmann, Kartic Subr, Nick K. Taylor, 2018, RSS 2018.

ELFI, a software package for likelihood-free inference

Aki Vehtari, Jukka Corander, Samuel Kaski, 2017, ICML 2017.

Efficient Bayesian Experimental Design for Implicit Models

Michael U. Gutmann, Steven Kleinegesse, S. Kleinegesse, 2018, AISTATS.

Resolving outbreak dynamics using Approximate Bayesian Computation for stochastic birth-death models

Jukka Corander, Samuel Kaski, Michael U. Gutmann, 2017 .

Robust Optimisation Monte Carlo pdf

Michael U. Gutmann, Borislav Ikonomov, Michael U Gutmann, 2019, AISTATS.

Proc. of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR)

Aapo Hyvärinen, Patrik O. Hoyer, Michael U. Gutmann, 2005 .

Workshop on Deep Learning and Unsupervised Feature Learning, NIPS

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2011 .

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

Charles A. Sutton, Michael U. Gutmann, Chris Russell, 2017, NIPS.

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

Aki Vehtari, Michael U. Gutmann, Pekka Marttinen, 2019, ArXiv.

Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI 2011)

Michael U. Gutmann, Jun-ichiro Hirayama, 2011 .

Adaptive Approximate Bayesian Computation Tolerance Selection

Jukka Corander, Michael U. Gutmann, Umberto Simola, 2019, Bayesian Analysis.

Likelihood-Free Inference by Ratio Estimation

Jukka Corander, Owen Thomas, Samuel Kaski, 2016, Bayesian Analysis.

On the identifiability of transmission dynamic models for infectious diseases

Jukka Corander, Samuel Kaski, Michael U. Gutmann, 2015 .

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

Michael U. Gutmann, Traiko Dinev, Traiko Dinev, 2018, ArXiv.

Generative Ratio Matching Networks

Kai Xu, Michael U. Gutmann, Charles Sutton, 2020, ICLR.

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

Michael U. Gutmann, Steven Kleinegesse, 2020, ICML.

ELFI: Engine for Likelihood Free Inference

Aki Vehtari, Jukka Corander, Samuel Kaski, 2016, J. Mach. Learn. Res..

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

Michael U. Gutmann, Jukka Corander, J. Corander, 2015, J. Mach. Learn. Res..

Bayesian Optimal Experimental Design for Simulator Models of Cognition pdf

Michael U. Gutmann, Steven Kleinegesse, Simon Valentin, 2021, ArXiv.

Variational Gibbs inference for statistical model estimation from incomplete data

Michael U. Gutmann, Vaidotas Simkus, Benjamin Rhodes, 2021 .

Likelihood-Free Inference by Ratio Estimation

Jukka Corander, Samuel Kaski, Michael U. Gutmann, 2016, Bayesian Analysis.

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

Michael U. Gutmann, Tom Rainforth, Steven Kleinegesse, 2021, NeurIPS.

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

Michael U. Gutmann, Steven Kleinegesse, S. Kleinegesse, 2021, ArXiv.

Likelihood-free inference via classification

Jukka Corander, Samuel Kaski, Michael U. Gutmann, 2014, Statistics and Computing.

Telescoping Density-Ratio Estimation pdf

Michael U. Gutmann, Kai Xu, Benjamin Rhodes, 2020, NeurIPS.