Atlas includes a strategy based on BO and variance-based active learning 110 to identify sets of parameters from X that result in average-best performance over a set of variables S = {s i } N i=1 . Instead of optimizing objective function f (x), the objective f (x, s) = 1 N N i=1 f (x, s i ) is targeted. Our strategy is inspired by the approach reported by Angello et al., which was used to design chemical reaction conditions resulting in high yields across a range of substrates. Importantly, this approach alleviates one from having to measure the full objective function for each recommended set of parameters, which can become costly when the number of general parameter options are large.