240 citations · 503 across the 31 of their papers we have counts for
5 papers · 1 filter
The Information Bottleneck EM Algorithm
Gal Elidan, Nir Friedman
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using…
"Ideal Parent" Structure Learning for Continuous Variable Networks
Iftach Nachman, Gal Elidan, Nir Friedman
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedu…
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing
Gal Elidan, Ian McGraw, Daphne Koller
Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its ge…
Convex Point Estimation using Undirected Bayesian Transfer Hierarchies
Gal Elidan, Ben Packer, Geremy Heitz +1
When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Bay…
Inference-less Density Estimation using Copula Bayesian Networks
Gal Elidan
We consider learning continuous probabilistic graphical models in the face of missing data. For non-Gaussian models, learning the parameters and structure of such models depends on…