DAGS - Daphne Koller's Research Group working on Probabilistic Reasoning with Bayesian Networks, Markov Decision Processes and Probabilistic Relational Models...
We believe that a good representation must also support effective inference and learning algorithms. Hence, our work is also highly focused on these topics. We have worked on exact and approximate inference algorithms for these representations, and on approaches for learning these models from data. On the inference side, we have done a lot of work on inference in dynamic Bayesian networks, inference in hybrid Bayesian networks, decision making in factored MDPs, and inference for large scale models such as those generated by a PRM or an OOBN. On the learning side, we have done a lot of work on learning probabilistic models from relational databases, on active learning of probabilistic models (where the learner can query for particular types of instances), and on learning utility functions from data.