Random linear network codes can be designed and implemented in a distributed manner, with low computational complexity. However, these codes are classically implemented [1] over fi...
The study of self-testing/correcting programs was introduced in [8] in order to allow one to use program P to compute function f without trusting that P works correctly. A self-te...
Peter Gemmell, Richard J. Lipton, Ronitt Rubinfeld...
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linea...
This paper presents a novel paradigm for learning languages that consists of mapping strings to an appropriate high-dimensional feature space and learning a separating hyperplane i...