We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
In this paper we investigate the feasibility and efficiency of mapping XML data and access control policies onto relational and native XML databases for storage and querying. We de...
Lazaros Koromilas, George Chinis, Irini Fundulaki,...
We have implemented an information flow framework for the Java Virtual Machine that combines static and dynamic techniques to capture not only explicit flows, but also implicit ...
Parallelization of sequential programs is often daunting because of the substantial development cost involved. Various solutions have been proposed to address this concern, includ...
Harnish Botadra, Qiong Cheng, Sushil K. Prasad, Er...
Most formulations of Reinforcement Learning depend on a single reinforcement reward value to guide the search for the optimal policy solution. If observation of this reward is rar...