Designing over uncertain outcomes with stochastic sampling Bayesian optimization
This work addresses optimization problems with stochasticity in scientific and engineering domains, offering an incremental improvement over existing Bayesian optimization methods.
The authors tackled optimization under uncertainty by proposing stochastic sampling Bayesian optimization (SSBO), a framework that adapts Bayesian optimization to handle stochastic outcomes, and demonstrated its effectiveness on standard problems and a bioengineering application.
Optimization is becoming increasingly common in scientific and engineering domains. Oftentimes, these problems involve various levels of stochasticity or uncertainty in generating proposed solutions. Therefore, optimization in these scenarios must consider this stochasticity to properly guide the design of future experiments. Here, we adapt Bayesian optimization to handle uncertain outcomes, proposing a new framework called stochastic sampling Bayesian optimization (SSBO). We show that the bounds on expected regret for an upper confidence bound search in SSBO resemble those of earlier Bayesian optimization approaches, with added penalties due to the stochastic generation of inputs. Additionally, we adapt existing batch optimization techniques to properly limit the myopic decision making that can arise when selecting multiple instances before feedback. Finally, we show that SSBO techniques properly optimize a set of standard optimization problems as well as an applied problem inspired by bioengineering.