Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis
This addresses the need for flexible sampling methods in fields like design of experiments and graphics, though it appears incremental as it modifies existing algorithms rather than introducing a new paradigm.
The paper tackles the problem of adapting sampling techniques to meet diverse constraints like non-rectangular domains or incremental sampling by proposing intelligent sampling algorithms tailored to specific needs. It shows that simple modified algorithms outperform existing sophisticated ones in surrogate modeling, hyperparameter optimization, and data analysis, leading to better resource efficiency.
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.