Bayesian experimental design using regularized determinantal point processes

The authors establish a fundamental connection between Bayesian experimental design and determinantal point processes, showing that a regularized DPP produces an unbiased estimator whose expected value under several classical optimality criteria (A-, C-, D-, V-optimality) can be characterized exactly, and give efficient algorithms based on this connection.