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José A. Gámez

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AAAI Conference 2026 Conference Paper

Bayesian Network Structural Consensus via Greedy Min-Cut Analysis

Pablo Torrijos
Jose M. Puerta
Juan A. Aledo
José A. Gámez

This paper presents the Min-Cut Bayesian Network Consensus (MCBNC) algorithm, a greedy method for structural consensus of Bayesian Networks (BNs), with applications in federated learning and model aggregation. MCBNC prunes weak edges from an initial unrestricted fusion using a structural score based on min-cut analysis, integrated into a modified Backward Equivalence Search (BES) phase of the Greedy Equivalence Search (GES) algorithm. The score quantifies edge support across input networks and is computed using max-flow. Unlike methods with fixed treewidth bounds, MCBNC introduces a pruning threshold θ that can be selected post hoc using only structural information. Experiments on real-world BNs show that MCBNC yields sparser, more accurate consensus structures than both canonical fusion and the input networks. The method is scalable, data-agnostic, and well-suited for distributed or federated structural learning of BNs or causal discovery.

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EAAI Journal 2023 Journal Article

FEDA-NRP: A fixed-structure multivariate estimation of distribution algorithm to solve the multi-objective Next Release Problem with requirements interactions

Víctor Pérez-Piqueras
Pablo Bermejo
José A. Gámez

In the development of a software product, the Next Release Problem is the selection of the most appropriate subset of requirements (tasks) to include in the next release of the product, such that the selected subset maximises the overall satisfaction of the stakeholders and minimises the total cost. Furthermore, in most cases, requirements or tasks cannot be developed independently, as there are dependencies between them, which must be respected in the selection for the next release. In this paper, we approach the Next Release Problem as a constrained bi-objective optimisation problem. The main contribution is the design of an Estimation of Distribution Algorithm that exploits domain knowledge, i. e. the dependencies between the requirements, to define the structure of a Bayesian network that models the relationships between the binary variables (requirements) to be optimised. The use of a Bayesian network with a fixed structure reduces the complexity of the search process, since it is unnecessary to learn the structure at each iteration of the algorithm. Moreover, it ensures that the sampled individuals are always valid with respect to the required dependencies. The second main contribution is the generation of a corpus of synthetic datasets with cost estimations derived from agile and classic management methodologies. Standard multi-objective metrics are computed in order to assess our proposal and compare it with other evolutionary multi-criterion optimisation algorithms, determining that it is the optimal choice when dealing with complex datasets.

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JMLR Journal 2019 Journal Article

spark-crowd: A Spark Package for Learning from Crowdsourced Big Data

Enrique G. Rodrigo
Juan A. Aledo
José A. Gámez

As the data sets increase in size, the process of manually labeling data becomes unfeasible by small groups of experts. Thus, it is common to rely on crowdsourcing platforms which provide inexpensive, but noisy, labels. Although implementations of algorithms to tackle this problem exist, none of them focus on scalability, limiting the area of application to relatively small data sets. In this paper, we present spark-crowd, an Apache Spark package for learning from crowdsourced data with scalability in mind. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2019. ( edit, beta )

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