Intelligent sampling for surrogate modeling, hyperparameter
  optimization, and data analysis
Chandrika Kamath
Sampling techniques are used in many fields, including design of experiments,
image processing, and graphics. The techniques in each field are designed to
meet the constraints specific to that field such as uniform coverage of the
range of each dimension or random samples that are at least a certain distance
apart from each other. When an application imposes new constraints, for
example, by requiring samples in a non-rectangular domain or the addition of
new samples to an existing set, a common solution is to modify the algorithm
currently in use, often with less than satisfactory results. As an alternative,
we propose the concept of intelligent sampling, where we devise algorithms
specifically tailored to meet our sampling needs, either by creating new
algorithms or by modifying suitable algorithms from other fields. Surprisingly,
both qualitative and quantitative comparisons indicate that some relatively
simple algorithms can be easily modified to meet the many sampling requirements
of surrogate modeling, hyperparameter optimization, and data analysis; these
algorithms outperform their more sophisticated counterparts currently in use,
resulting in better use of time and computer resources.