We consider apprenticeship learning—learning from expert demonstrations—in the setting of large, complex domains. Past work in apprenticeship learning requires that the expert...
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
Learning from imbalanced data occurs frequently in many machine learning applications. One positive example to thousands of negative instances is common in scientific applications...
Abstract. Many computer vision problems such as object segmentation or reconstruction can be formulated in terms of labeling a set of pixels or voxels. In certain scenarios, we may...
We consider a quadratic programming (QP) problem (Π) of the form min xT Cx subject to Ax ≥ b where C ∈ Rn×n + , rank(C) = 1 and A ∈ Rm×n , b ∈ Rm . We present an FPTAS ...