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19 papers · 2 filters
Discriminative Probabilistic Models for Relational Data
Ben Taskar, Pieter Abbeel, Daphne Koller
In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classificati…
Reinforcement Learning with Partially Known World Dynamics
Christian R. Shelton
Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden stat…
Learning Hierarchical Object Maps Of Non-Stationary Environments with mobile robots
Dragomir Anguelov, Rahul Biswas, Daphne Koller +2
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In t…
Learning Module Networks
Eran Segal, Dana Pe'er, Aviv Regev +2
Methods for learning Bayesian network structure can discover dependency structure between observed variables, and have been shown to be useful in many applications. However, in dom…
Learning Continuous Time Bayesian Networks
Uri Nodelman, Christian R. Shelton, Daphne Koller
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic…
Multi-track Map Matching
Adel Javanmard, Maya Haridasan, Li Zhang
We study algorithms for matching user tracks, consisting of time-ordered location points, to paths in the road network. Previous work has focused on the scenario where the location…