José M. Peña

CoRR, 2023

Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies.

[BibT_eX]

[DOI]

CoRR, 2023

Factorization of the Partial Covariance in Singly-Connected Path Diagrams.

[BibT_eX]

[DOI]

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

ρ-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders.

[BibT_eX]

[DOI]

Sourabh Balgi

Adel Daoud

CoRR, 2022

Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows.

[BibT_eX]

[DOI]

Sourabh Balgi

Adel Daoud

CoRR, 2022

Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows.

[BibT_eX]

[DOI]

Sourabh Balgi

Adel Daoud

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding.

[BibT_eX]

[DOI]

CoRR, 2021

On the Non-Monotonicity of a Non-Differentially Mismeasured Binary Confounder.

[BibT_eX]

[DOI]

CoRR, 2021

2020

On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder.

[BibT_eX]

[DOI]

CoRR, 2020

Conditional Path Analysis in Singly-Connected Path Diagrams.

[BibT_eX]

[DOI]

CoRR, 2020

2018

Unifying Gaussian LWF and AMP Chain Graphs to Model Interference.

[BibT_eX]

[DOI]

CoRR, 2018

Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances.

[BibT_eX]

[DOI]

CoRR, 2018

Identification of Strong Edges in AMP Chain Graphs.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Unifying DAGs and UGs.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2018

2017

Causal effect identification in acyclic directed mixed graphs and gated models.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

Representing independence models with elementary triplets.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

Modelling regimes with Bayesian network mixtures.

[BibT_eX]

[DOI]

Proceedings of the 30th Annual Workshop of the Swedish Artificial Intelligence Society, 2017

Learning Causal AMP Chain Graphs.

[BibT_eX]

[DOI]

Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, 2017

Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs.

[BibT_eX]

[DOI]

Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, 2017

2016

On expressiveness of the chain graph interpretations.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2016

Learning marginal AMP chain graphs under faithfulness revisited.

[BibT_eX]

[DOI]

Manuel Gómez-Olmedo

Int. J. Approx. Reason., 2016

Gated Bayesian networks for algorithmic trading.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2016

Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Learning Acyclic Directed Mixed Graphs from Observations and Interventions.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

2015

Approximate Counting of Graphical Models via MCMC Revisited.

[BibT_eX]

[DOI]

Manuel Gómez-Olmedo

Int. J. Intell. Syst., 2015

Chain graph interpretations and their relations revisited.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2015

Corrigendum to "Marginal AMP chain graphs" [Int. J. Approx. Reason. 55 (5) (2014) 1185-1206].

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2015

Alternative Markov Properties for Acyclic Directed Mixed Graphs.

[BibT_eX]

[DOI]

CoRR, 2015

Learning Optimal Chain Graphs with Answer Set Programming.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

Every LWF and AMP Chain Graph Originates from a Set of Causal Models.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

Chain Graphs and Gene Networks.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Biomedical Knowledge Representation, 2015

2014

Marginal AMP chain graphs.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2014

Learning AMP chain graphs and some marginal models thereof under faithfulness.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2014

Learning Marginal AMP Chain Graphs under Faithfulness.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

Learning Gated Bayesian Networks for Algorithmic Trading.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

An inclusion optimal algorithm for chain graph structure learning.

[BibT_eX]

[DOI]

Jens Dalgaard Nielsen

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Reading dependencies from covariance graphs.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2013

DemocraticOP: A Democratic way of aggregating Bayesian network parameters.

[BibT_eX]

[DOI]

Kobra Etminani

Mahmoud Naghibzadeh

Int. J. Approx. Reason., 2013

Combinatorial Optimization by Learning and Simulation of Bayesian Networks

[BibT_eX]

[DOI]

CoRR, 2013

Error AMP Chain Graphs.

[BibT_eX]

[DOI]

Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence, 2013

Gated Bayesian Networks.

[BibT_eX]

[DOI]

Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence, 2013

Chain Graph Interpretations and Their Relations.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2013

Approximate Counting of Graphical Models via MCMC Revisited.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2013

2012

Learning AMP Chain Graphs under Faithfulness

[BibT_eX]

[DOI]

CoRR, 2012

2011

Faithfulness in Chain Graphs: The Gaussian Case.

[BibT_eX]

[DOI]

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Finding Consensus Bayesian Network Structures.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2011

Towards Optimal Learning of Chain Graphs

[BibT_eX]

[DOI]

CoRR, 2011

A Correction of "Deriving a Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes"

[BibT_eX]

[DOI]

CoRR, 2011

2010

On the Complexity of Discrete Feature Selection for Optimal Classification.

[BibT_eX]

[DOI]

Roland Nilsson

IEEE Trans. Pattern Anal. Mach. Intell., 2010

2009

An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2009

Faithfulness in chain graphs: The discrete case.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2009

2008

Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control.

[BibT_eX]

[DOI]

Proceedings of the Evolutionary Computation, 2008

2007

Approximate Counting of Graphical Models Via MCMC.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Consistent Feature Selection for Pattern Recognition in Polynomial Time.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2007

Towards scalable and data efficient learning of Markov boundaries.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2007

Detecting multivariate differentially expressed genes.

[BibT_eX]

[DOI]

BMC Bioinform., 2007

Reading Dependencies from Polytree-Like Bayesian Networks.

[BibT_eX]

[DOI]

Proceedings of the UAI 2007, 2007

2006

Identifying the Relevant Nodes Without Learning the Model.

[BibT_eX]

[DOI]

Proceedings of the UAI '06, 2006

Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity.

[BibT_eX]

[DOI]

Proceedings of the Third European Workshop on Probabilistic Graphical Models, 2006

Evaluating Feature Selection for SVMs in High Dimensions.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning: ECML 2006, 2006

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm.

[BibT_eX]

[DOI]

Proceedings of the Towards a New Evolutionary Computation, 2006

2005

Learning dynamic Bayesian network models via cross-validation.

[BibT_eX]

[DOI]

Johan Björkegren

Jesper Tegnér

Pattern Recognit. Lett., 2005

Editorial.

[BibT_eX]

[DOI]

Mach. Learn., 2005

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks.

[BibT_eX]

[DOI]

Evol. Comput., 2005

Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption.

[BibT_eX]

[DOI]

Johan Björkegren

Jesper Tegnér

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005

Growing Bayesian network models of gene networks from seed genes.

[BibT_eX]

[DOI]

Johan Björkegren

Jesper Tegnér

Proceedings of the ECCB/JBI'05 Proceedings, Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (Jornadas de BioInformática), Palacio de Congresos, Madrid, Spain, September 28, 2005

2004

Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering.

[BibT_eX]

[DOI]

Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2004

2003

On Local Optima in Learning Bayesian Networks.

[BibT_eX]

[DOI]

Jens Dalgaard Nielsen

Tomás Kocka

Proceedings of the UAI '03, 2003

2002

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction.

[BibT_eX]

[DOI]

Mach. Learn., 2002

Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms.

[BibT_eX]

[DOI]

Proceedings of the First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002, 2002

Benefits of Data Clustering in Multimodal Function Optimization via EDAs.

[BibT_eX]

[DOI]

Proceedings of the Estimation of Distribution Algorithms, 2002

2001

Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2001

Performance evaluation of compromise conditional Gaussian networks for data clustering.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2001

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks.

[BibT_eX]

[DOI]

Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000

An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2000

Combinatonal Optimization by Learning and Simulation of Bayesian Networks.

[BibT_eX]

[DOI]

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

1999

Learning Bayesian networks for clustering by means of constructive induction.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 1999

An empirical comparison of four initialization methods for the K-Means algorithm.

[BibT_eX]

[DOI]