In this paper, we develop a new approach to the robust beamforming for general-rank signal models. Our method is based on the worst-case performance optimization using a semi-de n...
Abstract. We propose a privacy-preserving formulation of a linear program whose constraint matrix is partitioned into groups of columns where each group of columns and its correspo...
Previous studies of program visualization have generally failed to provide convincing support for the benefits of algorithm animation in promoting the understanding of computatio...
Mihail Eduard Tudoreanu, Rong Wu, Ashley Hamilton-...
Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and i...
We develop an efficient incremental version of an existing cost-based filtering algorithm for the knapsack constraint. On a universe of n elements, m invocations of the algorith...
Irit Katriel, Meinolf Sellmann, Eli Upfal, Pascal ...