The success of applying policy gradient reinforcement learning (RL) to difficult control tasks hinges crucially on the ability to determine a sensible initialization for the poli...
Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthe...
In this paper we model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We describe a novel import-by-query algorithm that alterna...
Mustafa Al-Bakri, Manuel Atencia, Steffen Lalande,...
Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Sub...
Object recognition systems can be unreliable when run in isolation depending on only image based features, but their performance can be improved when taking scene context into acc...
Akshaya Thippur, Chris Burbridge, Lars Kunze, Mari...
This paper raises the question of collective decision making under possibilistic uncertainty; We study four egalitarian decision rules and show that in the context of a possibilis...
Increased interest in web-based education has spurred the proliferation of online learning environments. However, these platforms suffer from high dropout rates due to lack of su...
Sparse learning has been proven to be a powerful technique in supervised feature selection, which allows to embed feature selection into the classification (or regression) proble...
When scheduling tasks for field-deployable systems, our solutions must be robust to the uncertainty inherent in the real world. Although human intuition is trusted to balance rew...
Module extraction—the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature Σ—has found many applications in recent y...
Ana Armas Romero, Mark Kaminski, Bernardo Cuenca G...
Temperature Discovery Search (TDS) is a forward search method for computing or approximating the temperature of a combinatorial game. Temperature and mean are important concepts i...