A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a shared covariance function on input-dependent features...
Edwin V. Bonilla, Kian Ming Chai, Christopher K. I...
The advent of Web services has made automated workflow composition relevant to Web based applications. One technique that has received some attention, for automatically composing ...
Prashant Doshi, Richard Goodwin, Rama Akkiraju, Ku...
Traditional query processors generate full, accurate query results, either in batch or in pipelined fashion. We argue that this strict model is too rigid for exploratory queries o...
We address an approximation method for Gaussian process (GP) regression, where we approximate covariance by a block matrix such that diagonal blocks are calculated exactly while o...