Representing the behaviour of supervised classification learning algorithms by Bayesian networks. - Iñaki Inza; Pedro Larrañaga; Basilio Sierra; Ramon Etxeberria; José Antonio Lozano 001; José M. Peña 001 (Pattern Recognit. Lett., 1999)

An empirical comparison of four initialization methods for the K-Means algorithm. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Pattern Recognit. Lett., 1999)

Learning Bayesian networks for clustering by means of constructive induction. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Pattern Recognit. Lett., 1999)

Combinatonal Optimization by Learning and Simulation of Bayesian Networks. - Pedro Larrañaga; Ramon Etxeberria; José Antonio Lozano 001; José M. Peña 001 (UAI, 2000)

An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Pattern Recognit. Lett., 2000)

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks. - José M. Peña 001; I. Izarzugaza; José Antonio Lozano 001; E. Aldasoro; Pedro Larrañaga (AISTATS, 2001)

Performance evaluation of compromise conditional Gaussian networks for data clustering. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Int. J. Approx. Reason., 2001)

Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga; Iñaki Inza (IEEE Trans. Pattern Anal. Mach. Intell., 2001)

Benefits of Data Clustering in Multimodal Function Optimization via EDAs. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Probabilistic Graphical Models, 2002)

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Mach. Learn., 2002)

On Local Optima in Learning Bayesian Networks. - Jens Dalgaard Nielsen; Tomás Kocka; José M. Peña 001 (UAI, 2003)

Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2004)

Growing Bayesian network models of gene networks from seed genes. - José M. Peña 001; Johan Björkegren; Jesper Tegnér (ECCB/JBI, 2005)

Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption. - José M. Peña 001; Johan Björkegren; Jesper Tegnér (ECSQARU, 2005)

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Evol. Comput., 2005)

Editorial. - Pedro Larrañaga; José Antonio Lozano 001; José M. Peña 001; Iñaki Inza (Mach. Learn., 2005)

Learning dynamic Bayesian network models via cross-validation. - José M. Peña 001; Johan Björkegren; Jesper Tegnér (Pattern Recognit. Lett., 2005)

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm. - Victor Robles; José M. Peña 001; Pedro Larrañaga; María S. Pérez 001; Vanessa Herves (Towards a New Evolutionary Computation, 2006)

Evaluating Feature Selection for SVMs in High Dimensions. - Roland Nilsson; José M. Peña 001; Johan Björkegren; Jesper Tegnér (ECML, 2006)

Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. - José M. Peña 001; Roland Nilsson; Johan Björkegren; Jesper Tegnér (Probabilistic Graphical Models, 2006)

Identifying the Relevant Nodes Without Learning the Model. - José M. Peña 001; Roland Nilsson; Johan Björkegren; Jesper Tegnér (UAI, 2006)

Reading Dependencies from Polytree-Like Bayesian Networks. - José M. Peña 001 (UAI, 2007)

Detecting multivariate differentially expressed genes. - Roland Nilsson; José M. Peña 001; Johan Björkegren; Jesper Tegnér (BMC Bioinform., 2007)

Towards scalable and data efficient learning of Markov boundaries. - José M. Peña 001; Roland Nilsson; Johan Björkegren; Jesper Tegnér (Int. J. Approx. Reason., 2007)

Consistent Feature Selection for Pattern Recognition in Polynomial Time. - Roland Nilsson; José M. Peña 001; Johan Björkegren; Jesper Tegnér (J. Mach. Learn. Res., 2007)

Approximate Counting of Graphical Models Via MCMC. - José M. Peña 001 (AISTATS, 2007)

Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control. - José M. Peña 001 (EvoBIO, 2008)

Faithfulness in chain graphs: The discrete case. - José M. Peña 001 (Int. J. Approx. Reason., 2009)

An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. - José M. Peña 001; Roland Nilsson; Johan Björkegren; Jesper Tegnér (J. Mach. Learn. Res., 2009)

On the Complexity of Discrete Feature Selection for Optimal Classification. - José M. Peña 001; Roland Nilsson (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

A Correction of "Deriving a Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes" - José M. Peña 001 (CoRR, 2011)

Towards Optimal Learning of Chain Graphs - José M. Peña 001 (CoRR, 2011)

Finding Consensus Bayesian Network Structures. - José M. Peña 001 (J. Artif. Intell. Res., 2011)

Faithfulness in Chain Graphs: The Gaussian Case. - José M. Peña 001 (AISTATS, 2011)

Learning AMP Chain Graphs under Faithfulness - José M. Peña 001 (CoRR, 2012)

Approximate Counting of Graphical Models via MCMC Revisited. - José M. Peña 001 (CAEPIA, 2013)

Chain Graph Interpretations and Their Relations. - Dag Sonntag; José M. Peña 001 (ECSQARU, 2013)

Gated Bayesian Networks. - Marcus Bendtsen; José M. Peña 001 (SCAI, 2013)

Error AMP Chain Graphs. - José M. Peña 001 (SCAI, 2013)

Combinatorial Optimization by Learning and Simulation of Bayesian Networks - Pedro Larrañaga; Ramon Etxeberria; José Antonio Lozano 001; José M. Peña 001 (CoRR, 2013)

DemocraticOP: A Democratic way of aggregating Bayesian network parameters. - Kobra Etminani; Mahmoud Naghibzadeh; José M. Peña 001 (Int. J. Approx. Reason., 2013)

Reading dependencies from covariance graphs. - José M. Peña 001 (Int. J. Approx. Reason., 2013)

An inclusion optimal algorithm for chain graph structure learning. - José M. Peña 001; Dag Sonntag; Jens Dalgaard Nielsen (AISTATS, 2014)

Learning Gated Bayesian Networks for Algorithmic Trading. - Marcus Bendtsen; José M. Peña 001 (Probabilistic Graphical Models, 2014)

Learning Marginal AMP Chain Graphs under Faithfulness. - José M. Peña 001 (Probabilistic Graphical Models, 2014)

Learning AMP chain graphs and some marginal models thereof under faithfulness. - José M. Peña 001 (Int. J. Approx. Reason., 2014)

Marginal AMP chain graphs. - José M. Peña 001 (Int. J. Approx. Reason., 2014)

Chain Graphs and Gene Networks. - Dag Sonntag; José M. Peña 001 (Foundations of Biomedical Knowledge Representation, 2015)

Every LWF and AMP Chain Graph Originates from a Set of Causal Models. - José M. Peña 001 (ECSQARU, 2015)

Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. - José M. Peña 001 (ECSQARU, 2015)

Learning Optimal Chain Graphs with Answer Set Programming. - Dag Sonntag; Matti Järvisalo; José M. Peña 001; Antti Hyttinen (UAI, 2015)

Alternative Markov Properties for Acyclic Directed Mixed Graphs. - José M. Peña 001 (CoRR, 2015)

Corrigendum to "Marginal AMP chain graphs" [Int. J. Approx. Reason. 55 (5) (2014) 1185-1206]. - José M. Peña 001 (Int. J. Approx. Reason., 2015)

Chain graph interpretations and their relations revisited. - Dag Sonntag; José M. Peña 001 (Int. J. Approx. Reason., 2015)

Approximate Counting of Graphical Models via MCMC Revisited. - Dag Sonntag; José M. Peña 001; Manuel Gómez-Olmedo (Int. J. Intell. Syst., 2015)

Learning Acyclic Directed Mixed Graphs from Observations and Interventions. - José M. Peña 001 (Probabilistic Graphical Models, 2016)

Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs. - José M. Peña 001 (UAI, 2016)

Gated Bayesian networks for algorithmic trading. - Marcus Bendtsen; José M. Peña 001 (Int. J. Approx. Reason., 2016)

Learning marginal AMP chain graphs under faithfulness revisited. - José M. Peña 001; Manuel Gómez-Olmedo (Int. J. Approx. Reason., 2016)

On expressiveness of the chain graph interpretations. - Dag Sonntag; José M. Peña 001 (Int. J. Approx. Reason., 2016)

Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs. - Jose M. Peña 001 (AMBN, 2017)

Learning Causal AMP Chain Graphs. - Jose M. Peña 001 (AMBN, 2017)

Modelling regimes with Bayesian network mixtures. - Marcus Bendtsen; José M. Peña 001 (SAIS, 2017)

Representing independence models with elementary triplets. - José M. Peña 001 (Int. J. Approx. Reason., 2017)

Causal effect identification in acyclic directed mixed graphs and gated models. - José M. Peña 001; Marcus Bendtsen (Int. J. Approx. Reason., 2017)

Unifying DAGs and UGs. - Jose M. Peña 001 (PGM, 2018)

Identification of Strong Edges in AMP Chain Graphs. - Jose M. Peña 001 (UAI, 2018)

Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances. - Jose M. Peña 001 (CoRR, 2018)

Unifying Gaussian LWF and AMP Chain Graphs to Model Interference. - Jose M. Peña 001 (CoRR, 2018)

Conditional Path Analysis in Singly-Connected Path Diagrams. - Jose M. Peña 001 (CoRR, 2020)

On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder. - Jose M. Peña 001 (CoRR, 2020)

On the Non-Monotonicity of a Non-Differentially Mismeasured Binary Confounder. - Jose M. Peña 001 (CoRR, 2021)

Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding. - Jose M. Peña 001 (CoRR, 2021)

Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows. - Sourabh Balgi; José M. Peña 001; Adel Daoud (AAAI, 2022)

Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows. - Sourabh Balgi; Jose M. Peña 001; Adel Daoud (CoRR, 2022)

ρ-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders. - Sourabh Balgi; José M. Peña 001; Adel Daoud (CoRR, 2022)

Factorization of the Partial Covariance in Singly-Connected Path Diagrams. - Jose M. Peña 001 (CLeaR, 2023)

Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies. - Jose M. Peña 001 (CoRR, 2023)

On the Probability of Immunity. - Jose M. Peña 001 (CoRR, 2023)

ρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows. - Sourabh Balgi; José M. Peña 001; Adel Daoud (PGM, 2024)

Counterfactually-Equivalent Structural Causal Modelling Using Causal Graphical Normalizing Flows. - Sourabh Balgi; José M. Peña 001; Adel Daoud (PGM, 2024)

Alternative Measures of Direct and Indirect Effects. - José M. Peña 001 (PGM, 2024)

Deep Learning With DAGs. - Sourabh Balgi; Adel Daoud; José M. Peña 001; Geoffrey T. Wodtke; Jesse Zhou (CoRR, 2024)

Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding. - José M. Peña 001 (CoRR, 2024)

Sharp Bounds of the Causal Effect Under MNAR Confounding. - José M. Peña 001 (CoRR, 2024)

Sensitivity analysis to unobserved confounding with copula-based normalizing flows. - Sourabh Balgi; Marc Braun; José M. Peña 001; Adel Daoud (Int. J. Approx. Reason., 2025)