Bayesian experimental design (BED) provides a powerful and general framework for
optimizing the design of experiments. However, its deployment often poses substantial …

Abstract Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess engineering. While …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Bayesian approaches developed to solve the optimal design of sequential experiments are
mathematically elegant but computationally challenging. Recently, techniques using …

Causal discovery from observational and interventional data is challenging due to limited
data and non-identifiability which introduces uncertainties in estimating the underlying …

This work proposes “jointly amortized neural approximation”(JANA) of intractable likelihood
functions and posterior densities arising in Bayesian surrogate modeling and simulation …

We introduce a gradient-based approach for the problem of Bayesian optimal experimental
design to learn causal models in a batch setting—a critical component for causal discovery …

Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all
quantitative sciences and industrial areas. This development is driven by a combination of …

The ability to estimate the state of a human partner is an insufficient basis on which to build
cooperative agents. Also needed is an ability to predict how people adapt their behavior in …