Here at SigOpt, Gaussian processes and reproducing kernel Hilbert spaces (RKHS) are important components of our Bayesian optimization methodology. Our research into this topic often exists at the intersection of approximation theory and spatial statistics. We recently attended the SIAM Computational Science and Engineering 2017 meeting in Atlanta to continue learning about outstanding accomplishments in these and other exciting topics in computation.

In addition to participating in the career fair, we also organized a minisymposium to bring together experts in Gaussian processes and RKHS and discuss recent advances that are relevant to Bayesian optimization. First, we would like to extend our thanks to Jian Wu, Jie Chen and Greg Fasshauer for contributing talks to the session; their presentations provided a diverse set of insights into new strategies for improving the viability and performance of RKHS theory in Bayesian optimization. This post serves to discuss our ongoing collaboration with Dr. Fasshauer and explore the topic of his talk at a level suitable for a broad audience.