Michael U. Gutmann

56 papers A* 7A 5B 1Misc 1Journal 40Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	CFMI: Flow Matching for Missing Data Imputation. CoRR Vaidotas Simkus, Michael U. Gutmann
2025	J	`jnl`	Neural Mutual Information Estimation with Vector Copulas. CoRR Yanzhi Chen, Zijing Ou, Adrian Weller, Michael U. Gutmann
2025	J	`jnl`	Simulation-based Bayesian inference under model misspecification. CoRR Ryan P. Kelly, David J. Warne, David T. Frazier, David J. Nott, Michael U. Gutmann, Christopher C. Drovandi
2024	J	`jnl`	Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. CoRR Vaidotas Simkus, Michael U. Gutmann
2024	J	`jnl`	Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. Trans. Mach. Learn. Res. Vaidotas Simkus, Michael U. Gutmann
2023	J	`jnl`	Bayesian Optimization with Informative Covariance. Trans. Mach. Learn. Res. Afonso Eduardo, Michael U. Gutmann
2023	J	`jnl`	Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling. CoRR Vaidotas Simkus, Michael U. Gutmann
2023	J	`jnl`	Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling. Trans. Mach. Learn. Res. Vaidotas Simkus, Michael U. Gutmann
2023	J	`jnl`	Designing Optimal Behavioral Experiments Using Machine Learning. CoRR Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Peggy Seriès, Michael U. Gutmann, Christopher G. Lucas
2023	J	`jnl`	Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. CoRR Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
2023	J	`jnl`	Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. Trans. Mach. Learn. Res. Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann
2023	A*	`conf`	Is Learning Summary Statistics Necessary for Likelihood-free Inference? ICML Yanzhi Chen, Michael U. Gutmann, Adrian Weller
2023	J	`jnl`	Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data. J. Mach. Learn. Res. Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
2022	J	`jnl`	Bayesian Optimization with Informative Covariance. CoRR Afonso Eduardo, Michael U. Gutmann
2022	J	`jnl`	Enhanced gradient-based MCMC in discrete spaces. CoRR Benjamin Rhodes, Michael U. Gutmann
2022	J	`jnl`	Enhanced gradient-based MCMC in discrete spaces. Trans. Mach. Learn. Res. Benjamin Rhodes, Michael U. Gutmann
2022	J	`jnl`	Pen and Paper Exercises in Machine Learning. CoRR Michael U. Gutmann
2022	J	`jnl`	Statistical applications of contrastive learning. CoRR Michael U. Gutmann, Steven Kleinegesse, Benjamin Rhodes
2022	J	`jnl`	Systematic comparison of ranking aggregation methods for gene lists in experimental results. Bioinform. Bo Wang, Andy Law, Tim Regan, Nicholas Parkinson, Joby Cole, Clark D. Russell, David H. Dockrell, Michael U. Gutmann, J. Kenneth Baillie
2021	B	`conf`	Bayesian Experimental Design for Intractable Models of Cognition. CogSci Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Chris Lucas
2021	J	`jnl`	Bayesian Optimal Experimental Design for Simulator Models of Cognition. CoRR Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Christopher G. Lucas
2021	J	`jnl`	Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds. CoRR Steven Kleinegesse, Michael U. Gutmann
2021	A*	`conf`	Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. NeurIPS Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth
2021	J	`jnl`	Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth
2021	A*	`conf`	Neural Approximate Sufficient Statistics for Implicit Models. ICLR Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
2021	J	`jnl`	Variational Gibbs inference for statistical model estimation from incomplete data. CoRR Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
2020	A*	`conf`	Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. ICML Steven Kleinegesse, Michael U. Gutmann
2020	J	`jnl`	Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. CoRR Steven Kleinegesse, Michael U. Gutmann
2020	A*	`conf`	Generative Ratio Matching Networks. ICLR Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
2020	J	`jnl`	Neural Approximate Sufficient Statistics for Implicit Models. CoRR Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
2020	A	`conf`	Robust Optimisation Monte Carlo. AISTATS Borislav Ikonomov, Michael U. Gutmann
2020	J	`jnl`	Sequential Bayesian Experimental Design for Implicit Models via Mutual Information. CoRR Steven Kleinegesse, Christopher C. Drovandi, Michael U. Gutmann
2020	A	`conf`	Stir to Pour: Efficient Calibration of Liquid Properties for Pouring Actions. IROS Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr
2020	A*	`conf`	Telescoping Density-Ratio Estimation. NeurIPS Benjamin Rhodes, Kai Xu, Michael U. Gutmann
2020	J	`jnl`	Telescoping Density-Ratio Estimation. CoRR Benjamin Rhodes, Kai Xu, Michael U. Gutmann
2019	A	`conf`	Adaptive Gaussian Copula ABC. AISTATS Yanzhi Chen, Michael U. Gutmann
2019	J	`jnl`	Adaptive Gaussian Copula ABC. CoRR Yanzhi Chen, Michael U. Gutmann
2019	A	`conf`	Efficient Bayesian Experimental Design for Implicit Models. AISTATS Steven Kleinegesse, Michael U. Gutmann
2019	J	`jnl`	Parallel Gaussian process surrogate method to accelerate likelihood-free inference. CoRR Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen
2019	J	`jnl`	Robust Optimisation Monte Carlo. CoRR Borislav Ikonomov, Michael U. Gutmann
2019	J	`jnl`	To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions. CoRR Tatiana Lopez-Guevara, Rita Pucci, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr
2019	A	`conf`	Variational Noise-Contrastive Estimation. AISTATS Benjamin Rhodes, Michael U. Gutmann
2018	A*	`conf`	Conditional Noise-Contrastive Estimation of Unnormalised Models. ICML Ciwan Ceylan, Michael U. Gutmann
2018	J	`jnl`	Conditional Noise-Contrastive Estimation of Unnormalised Models. CoRR Ciwan Ceylan, Michael U. Gutmann
2018	J	`jnl`	Dynamic Likelihood-free Inference via Ratio Estimation (DIRE). CoRR Traiko Dinev, Michael U. Gutmann
2018	J	`jnl`	ELFI: Engine for Likelihood-Free Inference. J. Mach. Learn. Res. Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
2018	J	`jnl`	Efficient Bayesian Experimental Design for Implicit Models. CoRR Steven Kleinegesse, Michael U. Gutmann
2018	J	`jnl`	Likelihood-free inference via classification. Stat. Comput. Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander
2018	J	`jnl`	Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
2018	J	`jnl`	Variational Noise-Contrastive Estimation. CoRR Benjamin Rhodes, Michael U. Gutmann
2017	Misc	`conf`	Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation. CoRL Tatiana Lopez-Guevara, Nicholas K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr
2017	J	`jnl`	ELFI: Engine for Likelihood Free Inference. CoRR Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
2017	—	`conf`	VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton
2016	J	`jnl`	Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models. J. Mach. Learn. Res. Michael U. Gutmann, Jukka Corander
2014	J	`jnl`	Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. Neural Comput. Song Liu, John A. Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi Sugiyama
2013	—	`conf`	Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. ECML/PKDD (2) Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama