We consider computationally tractable methods for the experimental design problem, where
k out of n design points of dimension p are selected so that certain optimality criteria are …

The experimental design problem concerns the selection of k points from a potentially large
design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed …

We revisit the classical problem of optimal experimental design (OED) under a new
mathematical model grounded in a geometric motivation. Specifically, we introduce models …

We establish a fundamental connection between Bayesian experimental design and
determinantal point processes (DPPs). Experimental design is a classical task in …

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 describe the R package acebayes and demonstrate its use to find Bayesian optimal
experimental designs. A decision-theoretic approach is adopted, with the optimal design …

In an optimal design problem, we are given a set of linear experiments $ v_1,…,
v_n\in\mathbb {R}^ d $ and $ k\geq d $, and our goal is to select a set or a multiset …

Elfving's theorem is a major result in the theory of optimal experimental design, which gives
a geometrical characterization of c-optimality. In this paper, we extend this theorem to the …

Suppose an online platform wants to compare a treatment and control policy (eg, two
different matching algorithms in a ridesharing system, or two different inventory management …

This paper addresses the issue of designing experiments for a metamodel that needs to be
accurate for a certain level of the response value. Such a situation is common in constrained …