Proactive learning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications. Active learning seeks to select the m...
The aim of transfer learning is to accelerate learning in related domains. In reinforcement learning, many different features such as a value function and a policy can be transfer...
This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dicti...
We contribute a method for approximating users’ interruptibility costs to use for experience sampling and validate the method in an application that learns when to automatically ...
Stephanie Rosenthal, Anind K. Dey, Manuela M. Velo...
We present a low-cost, decentralized algorithm for ID management in distributed hash tables (DHTs) managed by a dynamic set of hosts. Each host is assigned an ID in the unit inter...