| 2023 | ICML | Is Learning Summary Statistics Necessary for Likelihood-free Inference? | Yanzhi Chen, Michael U. Gutmann, Adrian Weller |
| 2021 | CogSci | Bayesian Experimental Design for Intractable Models of Cognition. | Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Chris Lucas |
| 2021 | ICLR | Neural Approximate Sufficient Statistics for Implicit Models. | Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu |
| 2020 | AISTATS | Robust Optimisation Monte Carlo. | Borislav Ikonomov, Michael U. Gutmann |
| 2020 | ICLR | Generative Ratio Matching Networks. | Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton |
| 2020 | ICML | Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation. | Steven Kleinegesse, Michael U. Gutmann |
| 2020 | IROS | 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 |
| 2019 | AISTATS | Adaptive Gaussian Copula ABC. | Yanzhi Chen, Michael U. Gutmann |
| 2019 | AISTATS | Efficient Bayesian Experimental Design for Implicit Models. | Steven Kleinegesse, Michael U. Gutmann |
| 2019 | AISTATS | Variational Noise-Contrastive Estimation. | Benjamin Rhodes, Michael U. Gutmann |
| 2018 | ICML | Conditional Noise-Contrastive Estimation of Unnormalised Models. | Ciwan Ceylan, Michael U. Gutmann |
| 2017 | CoRL | 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 |