We formulate and study a privacy guarantee to data owners, who share information with clients by publishing views of a proprietary database. The owner identifies the sensitive pro...
We present an anytime multiagent learning approach to satisfy any given optimality criterion in repeated game self-play. Our approach is opposed to classical learning approaches fo...
As tracking systems become more effective at reliably tracking multiple objects over extended periods of time within single camera views and across overlapping camera views, incre...
In this work we take a novel view of nonlinear manifold learning. Usually, manifold learning is formulated in terms of finding an embedding or `unrolling' of a manifold into ...
We propose an algorithm for automatically obtaining a segmentation of a rigid object in a sequence of images that are calibrated for camera pose and intrinsic parameters. Until re...
Neill D. F. Campbell, George Vogiatzis, Carlos Her...