Abstract Machine learning models have led to remarkable progress in visual recognition. A key driving factor for this progress is the abundance of labeled data. Over the years, researchers have spent a lot of effort curating visual data and carefully labeling it. However, moving forward, it seems impossible to annotate the vast amounts of visual […]

Abstract As machine learning becomes more ubiquitous, clustering has evolved from primarily a data analysis tool into an integrated component of complex machine learning systems, including those involving dimensionality reduction, anomaly detection, network analysis, image segmentation and classifying groups of data. With this integration into multi-stage systems comes a need to better understand interactions between […]

Abstract: In complex dynamic multi-robot domains, we have a set of individual robots that must coordinate together through a team planner that inevitably makes assumptions based on probabilities about the state of world and the actions of the individuals. Eventually, the individuals may encounter failures, because the team planner’s models of the states and actions [...]

Abstract: For this thesis, we propose to study how to automatically combine multiple neighborhood-based heuristics. For most computationally challenging problems, there exists multiple heuristics, and it is generally the case that any such heuristic exploits only a limited number of aspects among all the possible problem characteristics that we can think of. As a result, [...]

Abstract: A grand vision in robotics is that of a future wherein robots are integrated in daily human life just as smart phones are today. Such pervasive integration of robots would greatly benefit from faster design and manufacturing of robots that cater to individual needs. However, robots of today often take years to be created […]