Learning useful and predictable features from past workloads and exploiting them well is a major source of improvement in many operating system problems. We review known parallel ...
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while carrying out their work activities. It does this by associating with...
Jianqiang Shen, Lida Li, Thomas G. Dietterich, Jon...
— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of pla...
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework wh...
Kellen Gillespie, Justin Karneeb, Stephen Lee-Urba...