  Sampling one or more effective solutions from large search spaces is a
recurring idea in machine learning, and sequential optimization has become a
popular solution. Typical examples include data summarization, sample mining
for predictive modeling and hyper-parameter optimization. Existing solutions
attempt to adaptively trade-off between global exploration and local
exploitation, wherein the initial exploratory sample is critical to their
success. While discrepancy-based samples have become the de facto approach for
exploration, results from computer graphics suggest that coverage-based
designs, e.g. Poisson disk sampling, can be a superior alternative. In order to
successfully adopt coverage-based sample designs to ML applications, which were
originally developed for 2-d image analysis, we propose fundamental advances by
constructing a parameterized family of designs with provably improved coverage
characteristics, and by developing algorithms for effective sample synthesis.
Using experiments in sample mining and hyper-parameter optimization for
supervised learning, we show that our approach consistently outperforms
existing exploratory sampling methods in both blind exploration, and sequential
search with Bayesian optimization.
