Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an ex...
The notion of algorithmic stability has been used effectively in the past to derive tight generalization bounds. A key advantage of these bounds is that they are designed for spec...
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
A critical aspect of any explanation module is the set of user questions the system will be able to address. However, there has been relatively little work on listing and organizi...
Deborah L. McGuinness, Alyssa Glass, Michael Wolve...
We present a design for an automated theorem prover that controls its search based on ideas from several areas of artificial intelligence (AI). The combination of case-based reaso...