Efficient Bayesian Experimental Design for Implicit Models. - Steven Kleinegesse; Michael U. Gutmann (AISTATS, 2019)

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

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

Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks. - Jack Griffiths; Steven Kleinegesse; D. Saunders; R. Taylor; Antonin Vacheret (Mach. Learn. Sci. Technol., 2020)

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

Domain Knowledge in A*-Based Causal Discovery. - Steven Kleinegesse; Andrew R. Lawrence; Hana Chockler (CoRR, 2022)

Designing Optimal Behavioral Experiments Using Machine Learning. - Simon Valentin; Steven Kleinegesse; Neil R. Bramley; Peggy Seriès; Michael U. Gutmann; Christopher G. Lucas (CoRR, 2023)