We introduce a new approach to characterizing the unobserved portion of a distribution, which provides sublinear-sample additive estimators for a class of properties that includes...
This work presents the application of theory revision to the design of distributed databases to automatically revise a heuristic-based algorithm (called analysis algorithm) through...
We consider the effect of network throughput on the convergence of a specific class of distributed averaging algorithms, called consensus algorithms. These algorithms rely on itera...
We define TTD-MDPs, a novel class of Markov decision processes where the traditional goal of an agent is changed from finding an optimal trajectory through a state space to realiz...
David L. Roberts, Mark J. Nelson, Charles Lee Isbe...
We describe a novel simple and highly scalable semi-supervised method called Word-Class Distribution Learning (WCDL), and apply it the task of information extraction (IE) by utili...
Yanjun Qi, Ronan Collobert, Pavel Kuksa, Koray Kav...