We study the problem of auditing databases which support statistical sum queries to protect the security of sensitive information; we focus on the special case in which the sensit...
Jon M. Kleinberg, Christos H. Papadimitriou, Prabh...
We study the problem of context-sensitive ranking for document retrieval, where a context is defined as a sub-collection of documents, and is specified by queries provided by do...
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Inde...
Le Song, Alexander J. Smola, Arthur Gretton, Karst...
Abstract. The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data densi...
We propose a sequential Monte Carlo (SMC)-based motif discovery algorithm that can efficiently detect motifs in datasets containing a large number of sequences. The statistical di...