Reinforcement learning covers a broad category of control problems in which the learning agent interacts with the environment in order to learn to maximize the collected utility. Such exploratory interaction is often costly, encouraging sample-efficient algorithms to be used in…

The construction of accurate predictive models over sequence data is of fundamental importance in the pursuit of developing artificial agents that are capable of (near)-optimal sequential decision-making in disparate environments.If such predictive models are available, they can be exploited by…

This thesis explores the challenges involved in building autonomous exploration and monitoring systems, and makes contributions on four fronts: describing the semantic context of the collected data, summarizing this information, deciding where to collect this data, and making optimal online…

Reference documentation is an important source of information on API usage. Programmers, however, can easily overlook reference information because of its tedious nature, and because the information they seek can be buried among irrelevant or boiler-plate text. We propose to…

Positron emission tomography (PET) imaging is becoming increasingly popular for understanding brain function. This thesis addresses two problems related to PET images: binding potential (BP) computation and pairwise PET image registration. We first investigate the influence of several computational choices…

Modern information systems, consisting of an application server tier and a database tier, offer several consistency guarantees for accessing data where strong consistency is traded for better performance or higher availability. However, it is often not clear how an application…