We consider the problem of estimating the policy gradient in Partially Observable Markov Decision Processes (POMDPs) with a special class of policies that are based on Predictive ...
Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the prob...
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
Chris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan...
Two notions of optimality have been explored in previous work on hierarchical reinforcement learning (HRL): hierarchical optimality, or the optimal policy in the space defined by ...
We present a novel histogram method for statistically characterizing the appearance of deformable models. In deformable model segmentation, appearance models measure the likelihoo...
Robert E. Broadhurst, Joshua Stough, Stephen M. Pi...