We propose a clustering algorithm that effectively utilizes feature order preferences, which have the form that feature s is more important than feature t. Our clustering formulati...
Jun Sun, Wenbo Zhao, Jiangwei Xue, Zhiyong Shen, Y...
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
Abstract. In this paper, we propose a novel preference-constrained approach to k-anonymisation. In contrast to the existing works on kanonymisation which attempt to satisfy a minim...
Many computational problems linked to reasoning under uncertainty can be expressed in terms of computing the marginal(s) of the combination of a collection of (local) valuation fun...
With the increasing interest in developing pervasive computing technologies there is growing recognition of the problems of maintaining user privacy. In the Daidalos pervasive sys...
Elizabeth Papadopoulou, Sarah McBurney, Nick K. Ta...