We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded b...
In this paper, we introduce a new method to recover from discrepancies in a general monitoring framework where the agent finds some explanations (points of failure) for discrepan...
Dynamically reconfigurable systems based on partial and dynamically reconfigurable FPGAs may have their functionality partially modified at run-time without stopping the operation...
Manuel G. Gericota, Gustavo R. Alves, Miguel L. Si...
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
This paper investigates hindsight optimization as an approach for leveraging the significant advances in deterministic planning for action selection in probabilistic domains. Hind...
Sung Wook Yoon, Alan Fern, Robert Givan, Subbarao ...