The majority of machine learning research has been focused on building models and inference techniques with sound mathematical properties and cutting edge performance. Little atte...
Been Kim, Kayur Patel, Afshin Rostamizadeh, Julie ...
In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost...
Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called d...
Paul A. Szerlip, Gregory Morse, Justin K. Pugh, Ke...
Heuristic search is a state-of-the-art approach to classical planning. Several heuristic families were developed over the years to automatically estimate goal distance information...
Alexander Shleyfman, Michael Katz 0001, Malte Helm...
Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the workerabilities by optimizing an objective function, for instance, by maximizing th...
In many learning tasks with structural properties, structural sparsity methods help induce sparse models, usually leading to better interpretability and higher generalization perf...
PDDL+ planning involves reasoning about mixed discretecontinuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique th...
Daniel Bryce, Sicun Gao, David J. Musliner, Robert...
Teams of mobile robots often need to divide up subtasks efficiently. In spatial domains, a key criterion for doing so may depend on distances between robots and the subtasks’ l...
Monte Carlo planning has been proven successful in many sequential decision-making settings, but it suffers from poor exploration when the rewards are sparse. In this paper, we im...
Sriram Srinivasan, Erik Talvitie, Michael H. Bowli...
Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context...