One of the main challenges in Grid computing is efficient allocation of resources (CPU-hours, network bandwidth, etc.) to the tasks submitted by users. Due to the lack of centrali...
We introduce flexible algorithms that can automatically learn mappings from images to actions by interacting with their environment. They work by introducing an image classifier i...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classif...
Cristian Bucila, Rich Caruana, Alexandru Niculescu...