Finite-horizon sequential experimental design (SED) arises naturally in many contexts,
including hyperparameter tuning in machine learning among more traditional settings …

The design of multiple experiments is commonly undertaken via suboptimal strategies, such
as batch (open-loop) design that omits feedback or greedy (myopic) design that does not …

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

Experimental design is a process of obtaining a product with target property via
experimentation. Bayesian optimization offers a sample-efficient tool for experimental design …

We develop a new computational approach for" focused" optimal Bayesian experimental
design with nonlinear models, with the goal of maximizing expected information gain in …

Many real-world experimental design problems (a) evaluate multiple experimental
conditions in parallel and (b) replicate each condition multiple times due to large and …

Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate
black-box functions. Most BO methods ignore how evaluation costs may vary over the …

Experimentation is key to learning about our world, but careful design of experiments is
critical to ensure resources are used efficiently to conduct discerning investigations …

We introduce a fully stochastic gradient based approach to Bayesian optimal experimental
design (BOED). Our approach utilizes variational lower bounds on the expected information …

Inspired by the increasing desire to efficiently tune machine learning hyper-parameters, in
this work we rigorously analyse conventional and nonconventional assumptions inherent to …