We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Solving complex global problems such as illegal immigration, border control, and terrorism requires government organizations at all levels to share not only data but, more importa...
Seema Degwekar, Jeff DePree, Howard W. Beck, Carla...
In this paper we describe a time management approach to distributed agent-based simulation. We propose a new time management policy by joining optimistic synchronization techniques...
We propose PASTE, the first differentially private aggregation algorithms for distributed time-series data that offer good practical utility without any trusted server. PASTE add...