  We present a new algorithm ASEBO for optimizing high-dimensional blackbox
functions. ASEBO adapts to the geometry of the function and learns optimal sets
of sensing directions, which are used to probe it, on-the-fly. It addresses the
exploration-exploitation trade-off of blackbox optimization with expensive
blackbox queries by continuously learning the bias of the lower-dimensional
model used to approximate gradients of smoothings of the function via
compressed sensing and contextual bandits methods. To obtain this model, it
leverages techniques from the emerging theory of active subspaces in the novel
ES blackbox optimization context. As a result, ASEBO learns the dynamically
changing intrinsic dimensionality of the gradient space and adapts to the
hardness of different stages of the optimization without external supervision.
Consequently, it leads to more sample-efficient blackbox optimization than
state-of-the-art algorithms. We provide theoretical results and test ASEBO
advantages over other methods empirically by evaluating it on the set of
reinforcement learning policy optimization tasks as well as functions from the
recently open-sourced Nevergrad library.
