José M. Peña

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 21

Top Topics

Graphical Models

Sensitivity Analysis

Conditional Independence

Structure Learning

Top Venues

Int. J. Approx. Reason.

Probabilistic Graphical Models

Publications

Sourabh Balgi, Adel Daoud, José M. Peña, Geoffrey T. Wodtke, Jesse Zhou
Deep Learning With DAGs. CoRR (2024)
José M. Peña
Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding. CoRR (2024)
Sourabh Balgi, José M. Peña, Adel Daoud
ρ-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders. CoRR (2022)
Sourabh Balgi, José M. Peña, Adel Daoud
Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows. AAAI (2022)
Marcus Bendtsen, José M. Peña
Modelling regimes with Bayesian network mixtures. SAIS (2017)
José M. Peña
Representing independence models with elementary triplets. Int. J. Approx. Reason. 88 (2017)
José M. Peña, Marcus Bendtsen
Causal effect identification in acyclic directed mixed graphs and gated models. Int. J. Approx. Reason. 90 (2017)
Dag Sonntag, José M. Peña
On expressiveness of the chain graph interpretations. Int. J. Approx. Reason. 68 (2016)
José M. Peña
Learning Acyclic Directed Mixed Graphs from Observations and Interventions. Probabilistic Graphical Models (2016)
José M. Peña, Marcus Bendtsen
Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models. CoRR (2016)
José M. Peña
Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs. UAI (2016)
Marcus Bendtsen, José M. Peña
Gated Bayesian networks for algorithmic trading. Int. J. Approx. Reason. 69 (2016)
José M. Peña, Manuel Gómez-Olmedo
Learning marginal AMP chain graphs under faithfulness revisited. Int. J. Approx. Reason. 68 (2016)
Dag Sonntag, Matti Järvisalo, José M. Peña, Antti Hyttinen
Learning Optimal Chain Graphs with Answer Set Programming. UAI (2015)
José M. Peña
Every LWF and AMP Chain Graph Originates from a Set of Causal Models. ECSQARU (2015)
José M. Peña
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. CoRR (2015)
Dag Sonntag, José M. Peña
Chain graph interpretations and their relations revisited. Int. J. Approx. Reason. 58 (2015)
José M. Peña
Corrigendum to "Marginal AMP chain graphs" [Int. J. Approx. Reason. 55 (5) (2014) 1185-1206]. Int. J. Approx. Reason. 66 (2015)
Dag Sonntag, José M. Peña, Manuel Gómez-Olmedo
Approximate Counting of Graphical Models via MCMC Revisited. Int. J. Intell. Syst. 30 (3) (2015)
José M. Peña
Alternative Markov Properties for Acyclic Directed Mixed Graphs. CoRR (2015)
José M. Peña
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. ECSQARU (2015)
José M. Peña
Learning Marginal AMP Chain Graphs under Faithfulness. Probabilistic Graphical Models (2014)
José M. Peña
Learning AMP chain graphs and some marginal models thereof under faithfulness. Int. J. Approx. Reason. 55 (4) (2014)
José M. Peña
Marginal AMP chain graphs. Int. J. Approx. Reason. 55 (5) (2014)
José M. Peña, Dag Sonntag, Jens Dalgaard Nielsen
An inclusion optimal algorithm for chain graph structure learning. AISTATS (2014)
Marcus Bendtsen, José M. Peña
Learning Gated Bayesian Networks for Algorithmic Trading. Probabilistic Graphical Models (2014)
José M. Peña
Approximate Counting of Graphical Models via MCMC Revisited. CAEPIA (2013)
Dag Sonntag, José M. Peña
Chain Graph Interpretations and Their Relations. ECSQARU (2013)
Marcus Bendtsen, José M. Peña
Gated Bayesian Networks. SCAI (2013)
José M. Peña
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness CoRR (2013)
José M. Peña
Approximate Counting of Graphical Models Via MCMC Revisited CoRR (2013)
Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña
Combinatorial Optimization by Learning and Simulation of Bayesian Networks CoRR (2013)
José M. Peña
Error AMP Chain Graphs. CoRR (2013)
Kobra Etminani, Mahmoud Naghibzadeh, José M. Peña
DemocraticOP: A Democratic way of aggregating Bayesian network parameters. Int. J. Approx. Reason. 54 (5) (2013)
José M. Peña
Reading dependencies from covariance graphs. Int. J. Approx. Reason. 54 (1) (2013)
José M. Peña
Error AMP Chain Graphs. SCAI (2013)
José M. Peña
Marginal AMP Chain Graphs CoRR (2013)
José M. Peña
Reading Dependencies from Polytree-Like Bayesian Networks CoRR (2012)
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
Identifying the Relevant Nodes Without Learning the Model. CoRR (2012)
Jens Dalgaard Nielsen, Tomas Kocka, José M. Peña
On Local Optima in Learning Bayesian Networks CoRR (2012)
José M. Peña
Learning AMP Chain Graphs under Faithfulness CoRR (2012)
José M. Peña
A Correction of "Deriving a Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes" CoRR (2011)
José M. Peña
Finding Consensus Bayesian Network Structures. J. Artif. Intell. Res. 42 (2011)
José M. Peña
Faithfulness in Chain Graphs: The Gaussian Case. AISTATS (2011)
José M. Peña
Towards Optimal Learning of Chain Graphs CoRR (2011)
José M. Peña
Reading Dependencies from Covariance Graphs CoRR (2010)
José M. Peña
Faithfulness in Chain Graphs: The Gaussian Case CoRR (2010)
José M. Peña, Roland Nilsson
On the Complexity of Discrete Feature Selection for Optimal Classification. IEEE Trans. Pattern Anal. Mach. Intell. 32 (8) (2010)
José M. Peña
Faithfulness in chain graphs: The discrete case. Int. J. Approx. Reason. 50 (8) (2009)
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. J. Mach. Learn. Res. 10 (2009)
José M. Peña
Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control. EvoBIO (2008)
José M. Peña
Approximate Counting of Graphical Models Via MCMC. AISTATS (2007)
José M. Peña
Reading Dependencies from Polytree-Like Bayesian Networks. UAI (2007)
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
Towards scalable and data efficient learning of Markov boundaries. Int. J. Approx. Reason. 45 (2) (2007)
Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
Detecting multivariate differentially expressed genes. BMC Bioinform. 8 (2007)
Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
Consistent Feature Selection for Pattern Recognition in Polynomial Time. J. Mach. Learn. Res. 8 (2007)
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
Identifying the Relevant Nodes Without Learning the Model. UAI (2006)
José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. Probabilistic Graphical Models (2006)
Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
Evaluating Feature Selection for SVMs in High Dimensions. ECML (2006)
José M. Peña, Johan Björkegren, Jesper Tegnér
Growing Bayesian network models of gene networks from seed genes. ECCB/JBI (2005)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. Evol. Comput. 13 (1) (2005)
José M. Peña, Johan Björkegren, Jesper Tegnér
Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption. ECSQARU (2005)
José M. Peña, Johan Björkegren, Jesper Tegnér
Learning dynamic Bayesian network models via cross-validation. Pattern Recognit. Lett. 26 (14) (2005)
Pedro Larrañaga, José Antonio Lozano, José M. Peña, Iñaki Inza
Editorial. Mach. Learn. 59 (3) (2005)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 12 (Supplement-1) (2004)
Jens Dalgaard Nielsen, Tomás Kocka, José M. Peña
On Local Optima in Learning Bayesian Networks. UAI (2003)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. Probabilistic Graphical Models (2002)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. Mach. Learn. 47 (1) (2002)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
Performance evaluation of compromise conditional Gaussian networks for data clustering. Int. J. Approx. Reason. 28 (1) (2001)
José M. Peña, José Antonio Lozano, Pedro Larrañaga, Iñaki Inza
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. IEEE Trans. Pattern Anal. Mach. Intell. 23 (6) (2001)
José M. Peña, I. Izarzugaza, José Antonio Lozano, E. Aldasoro, Pedro Larrañaga
Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks. AISTATS (2001)
Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña
Combinatonal Optimization by Learning and Simulation of Bayesian Networks. UAI (2000)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. Pattern Recognit. Lett. 21 (8) (2000)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
Learning Bayesian networks for clustering by means of constructive induction. Pattern Recognit. Lett. 20 (11-13) (1999)
José M. Peña, José Antonio Lozano, Pedro Larrañaga
An empirical comparison of four initialization methods for the K-Means algorithm. Pattern Recognit. Lett. 20 (10) (1999)
Iñaki Inza, Pedro Larrañaga, Basilio Sierra, Ramon Etxeberria, José Antonio Lozano, José M. Peña
Representing the behaviour of supervised classification learning algorithms by Bayesian networks. Pattern Recognit. Lett. 20 (11-13) (1999)