We present an algorithm for solving a broad class of online resource allocation . Our online algorithm can be applied in environments where abstract jobs arrive one at a time, and...
The availability of technology has seen the development of online asynchronous discussion boards for use in teaching and learning. This paper explores the use of this medium withi...
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
— In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to pre...
Axel Rottmann, Christian Plagemann, Peter Hilgers,...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...