Empirical analysis serves as an important complement to theoretical analysis for studying practical Bayesian optimization. Often empirical insights expose strengths and weaknesses inaccessible to theoretical analysis. We define two met-rics for comparing the performance of Bayesian optimization methods and propose a ranking mechanism for summarizing… (More)
Bayesian optimization is an elegant solution to the hyperparameter optimization problem in machine learning. Building a reliable and robust Bayesian optimization service requires careful testing methodology and sound statistical analysis. In this talk we will outline our development of an evaluation framework to rigorously test and measure the impact of… (More)
We show that under a small number of assumptions, it is possible to interpret truth in a context as a quantification over truth in 'atomic' or pointlike contexts, which are transparent to all the connectives. We discuss the necessary assumptions and suggest conditions under which they are intuitively reasonable.