  We present a new predictor combination algorithm that improves a given task
predictor based on potentially relevant reference predictors. Existing
approaches are limited in that, to discover the underlying task dependence,
they either require known parametric forms of all predictors or access to a
single fixed dataset on which all predictors are jointly evaluated. To overcome
these limitations, we design a new non-parametric task dependence estimation
procedure that automatically aligns evaluations of heterogeneous predictors
across disjoint feature sets. Our algorithm is instantiated as a robust
manifold diffusion process that jointly refines the estimated predictor
alignments and the corresponding task dependence. We apply this algorithm to
the relative attributes ranking problem and demonstrate that it not only
broadens the application range of predictor combination approaches but also
outperforms existing methods even when applied to classical predictor
combination settings.
