1 code implementation • 12 May 2023 • Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Peggy Seriès, Michael U. Gutmann, Christopher G. Lucas
As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model.
no code implementations • 17 Aug 2022 • Steven Kleinegesse, Andrew R. Lawrence, Hana Chockler
Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data.
no code implementations • 29 Apr 2022 • Michael U. Gutmann, Steven Kleinegesse, Benjamin Rhodes
The likelihood function plays a crucial role in statistical inference and experimental design.
1 code implementation • NeurIPS 2021 • Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models.
no code implementations • NeurIPS Workshop AI4Scien 2021 • Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Michael U. Gutmann, Christopher G. Lucas
Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data.
1 code implementation • 10 May 2021 • Steven Kleinegesse, Michael U. Gutmann
We introduce a framework for Bayesian experimental design (BED) with implicit models, where the data-generating distribution is intractable but sampling from it is still possible.
1 code implementation • 20 Mar 2020 • Steven Kleinegesse, Christopher Drovandi, Michael U. Gutmann
We address this gap in the literature by devising a novel sequential design framework for parameter estimation that uses the Mutual Information (MI) between model parameters and simulated data as a utility function to find optimal experimental designs, which has not been done before for implicit models.
1 code implementation • ICML 2020 • Steven Kleinegesse, Michael U. Gutmann
A fundamental question is how to design the experiments so that the collected data are most useful.
1 code implementation • 23 Oct 2018 • Steven Kleinegesse, Michael Gutmann
Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance.
no code implementations • WS 2018 • Jennifer Williams, Steven Kleinegesse, Ramona Comanescu, Oana Radu
We present our system description of input-level multimodal fusion of audio, video, and text for recognition of emotions and their intensities for the 2018 First Grand Challenge on Computational Modeling of Human Multimodal Language.