Iftach Nachman

JMLR Journal 2007 Journal Article

"Ideal Parent” Structure Learning for Continuous Variable Bayesian Networks

• Gal Elidan
• Iftach Nachman
• Nir Friedman

Bayesian networks in general, and continuous variable networks in particular, have become increasingly popular in recent years, largely due to advances in methods that facilitate automatic learning from data. Yet, despite these advances, the key task of learning the structure of such models remains a computationally intensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks in the presence of missing values or hidden variables, a scenario that is part of many real-life problems. In this work we present a general method for speeding structure search for continuous variable networks with common parametric distributions. We efficiently evaluate the approximate merit of candidate structure modifications and apply time consuming (exact) computations only to the most promising ones, thereby achieving significant improvement in the running time of the search algorithm. Our method also naturally and efficiently facilitates the addition of useful new hidden variables into the network structure, a task that is typically considered both conceptually difficult and computationally prohibitive. We demonstrate our method on synthetic and real-life data sets, both for learning structure on fully and partially observable data, and for introducing new hidden variables during structure search. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

UAI Conference 2004 Conference Paper

"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 procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.

UAI Conference 2000 Conference Paper

Gaussian Process Networks

• Nir Friedman
• Iftach Nachman

UAI Conference 1999 Conference Paper

Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm

• Nir Friedman
• Iftach Nachman
• Dana Pe'er

Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the search space is extremely large, such search procedures can spend most of the time examining candidates that are extremely unreasonable. This problem becomes critical when we deal with data sets that are large either in the number of instances, or the number of attributes. In this paper, we introduce an algorithm that achieves faster learning by restricting the search space. This iterative algorithm restricts the parents of each variable to belong to a small subset of candidates. We then search for a network that satisfies these constraints. The learned network is then used for selecting better candidates for the next iteration. We evaluate this algorithm both on synthetic and real-life data. Our results show that it is significantly faster than alternative search procedures without loss of quality in the learned structures.