By bringing diverse explanation methods into a common framework, this work (1) advances the conceptual understanding of these methods, revealing their shared local function approximation objective, ...

Because the ground-truth relationship between the hormone and metabolite concentration is unknown, we will use ML to attempt to learn a close approximation from the data by simpler functions.

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions ...

Submodular maximization generalizes many fundamental problems in discrete optimization, including Max-Cut in directed/undirected graphs, maximum coverage, maximum facility location, and marketing over ...