  While adaptive sensing has provided improved rates of convergence in sparse
regression and classification, results in nonparametric regression have so far
been restricted to quite specific classes of functions. In this paper, we
describe an adaptive-sensing algorithm which is applicable to general
nonparametric-regression problems. The algorithm is spatially adaptive, and
achieves improved rates of convergence over spatially inhomogeneous functions.
Over standard function classes, it likewise retains the spatial adaptivity
properties of a uniform design.
