Existing approaches to active learning are generally optimistic about their certainty with respect to data shift between labeled and unlabeled data. They assume that unknown datap...
We introduce partial lexicographic preference trees (PLPtrees) as a formalism for compact representations of preferences over combinatorial domains. Our main results concern the p...
Flexibility in agent scheduling increases the resilience of temporal plans in the face of new constraints. However, current metrics of flexibility ignore domain knowledge about h...
Jeb Brooks, Emilia Reed, Alexander Gruver, James C...
Fast and efficient learning over large bodies of commonsense knowledge is a key requirement for cognitive systems. Semantic web knowledge bases provide an important new resource o...
Users commonly use Web 2.0 platforms to post their opinions and their predictions about future events (e.g., the movement of a stock). Therefore, opinion mining can be used as a t...
Algorithmic reductions are one of the corner stones of theoretical computer science. Surprisingly, to-date, they have only played a limited role in machine learning. In this paper...
Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardn...
Sequential decision problems that involve multiple objectives are prevalent. Consider for example a driver of a semiautonomous car who may want to optimize competing objectives su...
To judge how much a pair of words (or texts) are semantically related is a cognitive process. However, previous algorithms for computing semantic relatedness are largely based on ...
This paper describes a learning-based approach for automatic derivation of word variant forms by the suffixation process. We employ the sequence labeling technique, which entails...
The abundance of algorithms developed to solve different problems has given rise to an important research question: How do we choose the best algorithm for a given problem? Known ...
Richard Jayadi Oentaryo, Stephanus Daniel Handoko,...