Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. - Song Liu 002; John A. Quinn; Michael U. Gutmann; Masashi Sugiyama (ECML/PKDD (2), 2013)

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. - Song Liu 002; John A. Quinn; Michael U. Gutmann; Taiji Suzuki; Masashi Sugiyama (Neural Comput., 2014)

Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models. - Michael U. Gutmann; Jukka Corander (J. Mach. Learn. Res., 2016)

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 (CoRL, 2017)

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. - Akash Srivastava; Lazar Valkov; Chris Russell 001; Michael U. Gutmann; Charles Sutton (NIPS, 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)

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

Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. - Akash Srivastava; Kai Xu 016; Michael U. Gutmann; Charles Sutton (CoRR, 2018)

Dynamic Likelihood-free Inference via Ratio Estimation (DIRE). - Traiko Dinev; Michael U. Gutmann (CoRR, 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)

Likelihood-free inference via classification. - Michael U. Gutmann; Ritabrata Dutta; Samuel Kaski; Jukka Corander (Stat. Comput., 2018)

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 (AISTATS, 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)

Parallel Gaussian process surrogate method to accelerate likelihood-free inference. - Marko Järvenpää; Michael U. Gutmann; Aki Vehtari; Pekka Marttinen (CoRR, 2019)

Robust Optimisation Monte Carlo. - Borislav Ikonomov; Michael U. Gutmann (AISTATS, 2020)

Generative Ratio Matching Networks. - Akash Srivastava; Kai Xu 016; Michael U. Gutmann; Charles Sutton (ICLR, 2020)

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

Telescoping Density-Ratio Estimation. - Benjamin Rhodes; Kai Xu 016; Michael U. Gutmann (NeurIPS, 2020)

Sequential Bayesian Experimental Design for Implicit Models via Mutual Information. - Steven Kleinegesse; Christopher C. Drovandi; Michael U. Gutmann (CoRR, 2020)

Bayesian Experimental Design for Intractable Models of Cognition. - Simon Valentin; Steven Kleinegesse; Neil R. Bramley; Michael U. Gutmann; Chris Lucas 001 (CogSci, 2021)

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

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. - Desi R. Ivanova; Adam Foster 001; Steven Kleinegesse; Michael U. Gutmann; Thomas Rainforth (NeurIPS, 2021)

Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds. - Steven Kleinegesse; Michael U. Gutmann (CoRR, 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)

Statistical applications of contrastive learning. - Michael U. Gutmann; Steven Kleinegesse; Benjamin Rhodes (CoRR, 2022)

Pen and Paper Exercises in Machine Learning. - Michael U. Gutmann (CoRR, 2022)

Enhanced gradient-based MCMC in discrete spaces. - Benjamin Rhodes; Michael U. Gutmann (Trans. Mach. Learn. Res., 2022)

Systematic comparison of ranking aggregation methods for gene lists in experimental results. - Bo Wang 118; Andy Law; Tim Regan; Nicholas Parkinson; Joby Cole; Clark D. Russell; David H. Dockrell; Michael U. Gutmann; J. Kenneth Baillie (Bioinform., 2022)

Is Learning Summary Statistics Necessary for Likelihood-free Inference? - Yanzhi Chen; Michael U. Gutmann; Adrian Weller (ICML, 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)

Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data. - Vaidotas Simkus; Benjamin Rhodes; Michael U. Gutmann (J. Mach. Learn. Res., 2023)

Bayesian Optimization with Informative Covariance. - Afonso Eduardo; 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)

Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. - Akash Srivastava; Seungwook Han; Kai Xu 016; Benjamin Rhodes; Michael U. Gutmann (Trans. Mach. Learn. Res., 2023)

Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. - Vaidotas Simkus; Michael U. Gutmann (Trans. Mach. Learn. Res., 2024)

Simulation-based Bayesian inference under model misspecification. - Ryan P. Kelly; David J. Warne; David T. Frazier; David J. Nott; Michael U. Gutmann; Christopher C. Drovandi (CoRR, 2025)

CFMI: Flow Matching for Missing Data Imputation. - Vaidotas Simkus; Michael U. Gutmann (CoRR, 2025)