Results 1  10
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40
Structure learning in random fields for heart motion abnormality detection
 In CVPR
, 2008
"... Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are difficult to interpret leading to high intraobserver variability. Previous work indicates that in order to approach this pr ..."
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Cited by 40 (5 self)
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Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are difficult to interpret leading to high intraobserver variability. Previous work indicates that in order to approach this problem, the interactions between the different heart regions and their overall influence on the clinical condition of the heart need to be considered. To do this, we propose a method for jointly learning the structure and parameters of conditional random fields, formulating these tasks as a convex optimization problem. We consider blockL1 regularization for each set of features associated with an edge, and formalize an efficient projection method to find the globally optimal penalized maximum likelihood solution. We perform extensive numerical experiments comparing the presented method with related methods that approach the structure learning problem differently. We verify the robustness of our method on echocardiograms collected in routine clinical practice at one hospital. 1.
Efficient Principled Learning of Thin Junction Trees
"... We present the first truly polynomial algorithm for PAClearning the structure of boundedtreewidth junction trees – an attractive subclass of probabilistic graphical models that permits both the compact representation of probability distributions and efficient exact inference. For a constant treewi ..."
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Cited by 30 (4 self)
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We present the first truly polynomial algorithm for PAClearning the structure of boundedtreewidth junction trees – an attractive subclass of probabilistic graphical models that permits both the compact representation of probability distributions and efficient exact inference. For a constant treewidth, our algorithm has polynomial time and sample complexity. If a junction tree with sufficiently strong intraclique dependencies exists, we provide strong theoretical guarantees in terms of KL divergence of the result from the true distribution. We also present a lazy extension of our approach that leads to very significant speed ups in practice, and demonstrate the viability of our method empirically, on several real world datasets. One of our key new theoretical insights is a method for bounding the conditional mutual information of arbitrarily large sets of variables with only polynomially many mutual information computations on fixedsize subsets of variables, if the underlying distribution can be approximated by a boundedtreewidth junction tree. 1
Learning graphical model structure using L1regularization paths
 In Proceedings of the 21st Conference on Artificial Intelligence (AAAI
, 2007
"... Sparsitypromoting L1regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability ..."
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Cited by 21 (1 self)
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Sparsitypromoting L1regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability of the MDL score, plus the ability to quickly compute entire regularization paths, allows us to efficiently pick the optimal regularization parameter on a pernode basis. Second, we show how to use L1 variable selection to select the Markov blanket, before a DAG search stage. Finally, we show how L1 variable selection can be used inside of an order search algorithm. The effectiveness of these L1based approaches are compared to current state of the art methods on 10 datasets.
Structure Learning of Bayesian Networks using Constraints
"... This paper addresses exact learning of Bayesian network structure from data and expert’s knowledge based on score functions that are decomposable. First, it describes useful properties that strongly reduce the time and memory costs of many known methods such as hillclimbing, dynamic programming and ..."
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Cited by 18 (1 self)
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This paper addresses exact learning of Bayesian network structure from data and expert’s knowledge based on score functions that are decomposable. First, it describes useful properties that strongly reduce the time and memory costs of many known methods such as hillclimbing, dynamic programming and sampling variable orderings. Secondly, a branch and bound algorithm is presented that integrates parameter and structural constraints with data in a way to guarantee global optimality with respect to the score function. It is an anytime procedure because, if stopped, it provides the best current solution and an estimation about how far it is from the global solution. We show empirically the advantages of the properties and the constraints, and the applicability of the algorithm to large data sets (up to one hundred variables) that cannot be handled by other current methods (limited to around 30 variables). 1.
The “ideal parent” structure learning for continuous variable networks
 Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence
, 2004
"... 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 hi ..."
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Cited by 14 (2 self)
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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. 1
Efficient structure learning of Bayesian networks using constraints
 Journal of Machine Learning Research
"... This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived fo ..."
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Cited by 13 (1 self)
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This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion), Akaike Information Criterion and Bayesian Dirichlet Criterion. Then a branchandbound algorithm is presented that integrates structural constraints with data in a way to guarantee global optimality. As an example, structural constraints are used to map the problem of structure learning in Dynamic Bayesian networks into a corresponding augmented Bayesian network. Finally, we show empirically the benefits of using the properties with stateoftheart methods and with the new algorithm, which is able to handle larger data sets than before.
Network inference from cooccurrences
, 2006
"... Abstract—The discovery of networks is a fundamental problem ..."
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Cited by 9 (0 self)
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Abstract—The discovery of networks is a fundamental problem
Learning Thin Junction Trees via Graph Cuts
"... Structure learning algorithms usually focus on the compactness of the learned model. However, for general compact models, both exact and approximate inference are still NPhard. Therefore, the focus only on compactness leads to learning models that require approximate inference techniques, thus redu ..."
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Cited by 8 (4 self)
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Structure learning algorithms usually focus on the compactness of the learned model. However, for general compact models, both exact and approximate inference are still NPhard. Therefore, the focus only on compactness leads to learning models that require approximate inference techniques, thus reducing their prediction quality. In this paper, we propose a method for learning an attractive class of models: boundedtreewidth junction trees, which permit both compact representation of probability distributions and efficient exact inference. Using Bethe approximation of the likelihood, we transform the problem of finding a good junction tree separator into a minimum cut problem on a weighted graph. Using the graph cut intuition, we present an efficient algorithm with theoretical guarantees for finding good separators, which we recursively apply to obtain a thin junction tree. Our extensive empirical evaluation demonstrates the benefit of applying exact inference using our models to answer queries. We also extend our technique to learning low treewidth conditional random fields, and demonstrate significant improvements over state of the art blockL1 regularization techniques. 1
E.A.Lee, ``Hierarchical Finite State Machines with Multiple Concurrency Models
 Laboratory, University of California, Berkeley, CA
, 1999
"... Abstract. There is an increasing interest in upgrading Bayesian networks to the relational case, resulting in socalled directed probabilistic logical models. In this paper we discuss how to learn nonrecursive directed probabilistic logical models from relational data. This problem has already been ..."
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Cited by 7 (3 self)
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Abstract. There is an increasing interest in upgrading Bayesian networks to the relational case, resulting in socalled directed probabilistic logical models. In this paper we discuss how to learn nonrecursive directed probabilistic logical models from relational data. This problem has already been tackled before by upgrading the structuresearch algorithm for learning Bayesian networks. In this paper we propose to upgrade another algorithm, namely orderingsearch, since for Bayesian networks this was found to work better than structuresearch. We have implemented both algorithms for the formalism Logical Bayesian Networks and are currently working on an experimental comparison of both algorithms.
Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering
, 2006
"... We present a new approach to learning the structure and parameters of a Bayesian network based on regularized estimation in an exponential family representation. Here we show that, given a fixed variable order, the optimal structure and parameters can be learned efficiently, even without restricting ..."
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Cited by 7 (2 self)
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We present a new approach to learning the structure and parameters of a Bayesian network based on regularized estimation in an exponential family representation. Here we show that, given a fixed variable order, the optimal structure and parameters can be learned efficiently, even without restricting the size of the parent variable sets. We then consider the problem of optimizing the variable order for a given set of features. This is still a computationally hard problem, but we present a convex relaxation that yields an optimal “soft” ordering in polynomial time. One novel aspect of the approach is that we do not perform a discrete search over DAG structures, nor over variable orders, but instead solve a continuous convex relaxation that can then be rounded to obtain a valid network structure. We conduct an experimental comparison against standard structure search procedures over standard objectives, which cope with local minima, and evaluate the advantages of using convex relaxations that reduce the effects of local minima.