The final form of this volume differs a lot from our initial project that was born in 2003. Our
collaboration that was initiated about 10 years before was then at a pace and we formulated …

Optimization of clinical trial designs can help investigators achieve higher quality results for
the given resource constraints. The present paper gives an overview of optimal designs for …

I have enjoyed very much writing this book. This is very important from a personal point of
view, but I believe this is the key of writing something interesting for the readers. One of the …

There are multiple possible cluster randomised trial designs that vary in when the clusters
cross between control and intervention states, when observations are made within clusters …

Abstract Machine learning algorithms often require large training sets to perform well, but
labeling such large amounts of data is not always feasible, as in many applications …

We propose a class of subspace ascent methods for computing optimal approximate
designs that covers existing algorithms as well as new and more efficient ones. Within this …

Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for
learning sequential decision-making policies. Different from Reinforcement Learning (RL) …

Let the design of an experiment be represented by an s-dimensional vector w of weights
with nonnegative components. Let the quality of w for the estimation of the parameters of the …

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be
successful in solving a wide variety of real and complicated optimization problems in …

Several common general purpose optimization algorithms are compared for finding A-and D-
optimal designs for different types of statistical models of varying complexity, including high …