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...
Abstract. In the Web environment, rich, diverse sources of heterogeneous and distributed data are ubiquitous. In fact, even the information characterizing a single entity - like, f...
Muhammad Intizar Ali, Reinhard Pichler, Hong Linh ...
Abstract. We present INDUS (Intelligent Data Understanding System), a federated, query-centric system for knowledge acquisition from autonomous, distributed, semantically heterogen...
Doina Caragea, Jyotishman Pathak, Jie Bao, Adrian ...
With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed,...
Doina Caragea, Jaime Reinoso, Adrian Silvescu, Vas...
The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image...