Optimal design for linear regression is a fundamental task in statistics. For finite design spaces, recent progress has shown that random designs drawn using proportional volume sampling (PVS) lead to approximation guarantees for A-optimal design. PVS strikes the balance between design nodes that jointly fill the design space, while marginally staying in regions of high mass under the solution of a relaxed convex version of the original problem. In this paper, we examine some of the statistical implications of a new variant of PVS for (possibly Bayesian) optimal design. Using point process machinery, we treat the case of a generic Polish design space. We show that not only are the A-optimality approximation guarantees preserved, but we obtain similar guarantees for D-optimal design that tighten recent results. Moreover, we show that PVS can be sampled in polynomial time. Unfortunately, in spite of its elegance and tractability, we demonstrate on a simple example that the practical implications of general PVS are likely limited. In the second part of the paper, we focus on applications and investigate the use of PVS as a subroutine for stochastic search heuristics. We demonstrate that PVS is a robust addition to the practitioner's toolbox, especially when the regression functions are nonstandard and the design space, while low-dimensional, has a complicated shape (e.g., nonlinear boundaries, several connected components).
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