We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (rand...
Rakesh Agrawal, Ramakrishnan Srikant, Dilys Thomas
This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on th...
We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the gen...
We introduce a novel data-driven mean-shift belief propagation
(DDMSBP) method for non-Gaussian MRFs, which
often arise in computer vision applications. With the aid
of scale sp...
—Data compression techniques such as null suppression and dictionary compression are commonly used in today’s database systems. In order to effectively leverage compression, it...
Stratos Idreos, Raghav Kaushik, Vivek R. Narasayya...