José M. Peña

85 papers A* 1A 10C 4Journal 52Unranked 15

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Sensitivity Analysis to Unobserved Confounding with Copula-based Normalizing Flows. CoRR Sourabh Balgi, Marc Braun, José M. Peña, Adel Daoud
2025	J	`jnl`	Sensitivity analysis to unobserved confounding with copula-based normalizing flows. Int. J. Approx. Reason. Sourabh Balgi, Marc Braun, José M. Peña, Adel Daoud
2024	—	`conf`	Alternative Measures of Direct and Indirect Effects. PGM José M. Peña
2024	—	`conf`	Counterfactually-Equivalent Structural Causal Modelling Using Causal Graphical Normalizing Flows. PGM Sourabh Balgi, José M. Peña, Adel Daoud
2024	J	`jnl`	Deep Learning With DAGs. CoRR Sourabh Balgi, Adel Daoud, José M. Peña, Geoffrey T. Wodtke, Jesse Zhou
2024	J	`jnl`	Sharp Bounds of the Causal Effect Under MNAR Confounding. CoRR José M. Peña
2024	J	`jnl`	Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding. CoRR José M. Peña
2024	—	`conf`	ρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows. PGM Sourabh Balgi, José M. Peña, Adel Daoud
2022	A*	`conf`	Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows. AAAI Sourabh Balgi, José M. Peña, Adel Daoud
2022	J	`jnl`	ρ-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders. CoRR Sourabh Balgi, José M. Peña, Adel Daoud
2017	J	`jnl`	Causal effect identification in acyclic directed mixed graphs and gated models. Int. J. Approx. Reason. José M. Peña, Marcus Bendtsen
2017	—	`conf`	Modelling regimes with Bayesian network mixtures. SAIS Marcus Bendtsen, José M. Peña
2017	J	`jnl`	Representing independence models with elementary triplets. Int. J. Approx. Reason. José M. Peña
2016	A	`conf`	Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs. UAI José M. Peña
2016	J	`jnl`	Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models. CoRR José M. Peña, Marcus Bendtsen
2016	J	`jnl`	Gated Bayesian networks for algorithmic trading. Int. J. Approx. Reason. Marcus Bendtsen, José M. Peña
2016	—	`conf`	Learning Acyclic Directed Mixed Graphs from Observations and Interventions. Probabilistic Graphical Models José M. Peña
2016	J	`jnl`	Learning marginal AMP chain graphs under faithfulness revisited. Int. J. Approx. Reason. José M. Peña, Manuel Gómez-Olmedo
2016	J	`jnl`	On expressiveness of the chain graph interpretations. Int. J. Approx. Reason. Dag Sonntag, José M. Peña
2015	J	`jnl`	Alternative Markov Properties for Acyclic Directed Mixed Graphs. CoRR José M. Peña
2015	J	`jnl`	Approximate Counting of Graphical Models via MCMC Revisited. Int. J. Intell. Syst. Dag Sonntag, José M. Peña, Manuel Gómez-Olmedo
2015	—	`ch.`	Chain Graphs and Gene Networks. Foundations of Biomedical Knowledge Representation Dag Sonntag, José M. Peña
2015	J	`jnl`	Chain graph interpretations and their relations revisited. Int. J. Approx. Reason. Dag Sonntag, José M. Peña
2015	J	`jnl`	Corrigendum to "Marginal AMP chain graphs" [Int. J. Approx. Reason. 55 (5) (2014) 1185-1206]. Int. J. Approx. Reason. José M. Peña
2015	C	`conf`	Every LWF and AMP Chain Graph Originates from a Set of Causal Models. ECSQARU José M. Peña
2015	C	`conf`	Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. ECSQARU José M. Peña
2015	J	`jnl`	Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. CoRR José M. Peña
2015	A	`conf`	Learning Optimal Chain Graphs with Answer Set Programming. UAI Dag Sonntag, Matti Järvisalo, José M. Peña, Antti Hyttinen
2014	A	`conf`	An inclusion optimal algorithm for chain graph structure learning. AISTATS José M. Peña, Dag Sonntag, Jens Dalgaard Nielsen
2014	J	`jnl`	Learning AMP chain graphs and some marginal models thereof under faithfulness. Int. J. Approx. Reason. José M. Peña
2014	—	`conf`	Learning Gated Bayesian Networks for Algorithmic Trading. Probabilistic Graphical Models Marcus Bendtsen, José M. Peña
2014	—	`conf`	Learning Marginal AMP Chain Graphs under Faithfulness. Probabilistic Graphical Models José M. Peña
2014	J	`jnl`	Marginal AMP chain graphs. Int. J. Approx. Reason. José M. Peña
2013	J	`jnl`	Approximate Counting of Graphical Models Via MCMC Revisited CoRR José M. Peña
2013	—	`conf`	Approximate Counting of Graphical Models via MCMC Revisited. CAEPIA José M. Peña
2013	C	`conf`	Chain Graph Interpretations and Their Relations. ECSQARU Dag Sonntag, José M. Peña
2013	J	`jnl`	Combinatorial Optimization by Learning and Simulation of Bayesian Networks CoRR Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña
2013	J	`jnl`	DemocraticOP: A Democratic way of aggregating Bayesian network parameters. Int. J. Approx. Reason. Kobra Etminani, Mahmoud Naghibzadeh, José M. Peña
2013	—	`conf`	Error AMP Chain Graphs. SCAI José M. Peña
2013	J	`jnl`	Error AMP Chain Graphs. CoRR José M. Peña
2013	—	`conf`	Gated Bayesian Networks. SCAI Marcus Bendtsen, José M. Peña
2013	J	`jnl`	Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness CoRR José M. Peña
2013	J	`jnl`	Marginal AMP Chain Graphs CoRR José M. Peña
2013	J	`jnl`	Reading dependencies from covariance graphs. Int. J. Approx. Reason. José M. Peña
2012	J	`jnl`	Identifying the Relevant Nodes Without Learning the Model. CoRR José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2012	J	`jnl`	Learning AMP Chain Graphs under Faithfulness CoRR José M. Peña
2012	J	`jnl`	On Local Optima in Learning Bayesian Networks CoRR Jens Dalgaard Nielsen, Tomas Kocka, José M. Peña
2012	J	`jnl`	Reading Dependencies from Polytree-Like Bayesian Networks CoRR José M. Peña
2011	J	`jnl`	A Correction of "Deriving a Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes" CoRR José M. Peña
2011	A	`conf`	Faithfulness in Chain Graphs: The Gaussian Case. AISTATS José M. Peña
2011	J	`jnl`	Finding Consensus Bayesian Network Structures. J. Artif. Intell. Res. José M. Peña
2011	J	`jnl`	Towards Optimal Learning of Chain Graphs CoRR José M. Peña
2010	J	`jnl`	Faithfulness in Chain Graphs: The Gaussian Case CoRR José M. Peña
2010	J	`jnl`	On the Complexity of Discrete Feature Selection for Optimal Classification. IEEE Trans. Pattern Anal. Mach. Intell. José M. Peña, Roland Nilsson
2010	J	`jnl`	Reading Dependencies from Covariance Graphs CoRR José M. Peña
2009	J	`jnl`	An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. J. Mach. Learn. Res. José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2009	J	`jnl`	Faithfulness in chain graphs: The discrete case. Int. J. Approx. Reason. José M. Peña
2008	—	`conf`	Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control. EvoBIO José M. Peña
2007	A	`conf`	Approximate Counting of Graphical Models Via MCMC. AISTATS José M. Peña
2007	J	`jnl`	Consistent Feature Selection for Pattern Recognition in Polynomial Time. J. Mach. Learn. Res. Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
2007	J	`jnl`	Detecting multivariate differentially expressed genes. BMC Bioinform. Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
2007	A	`conf`	Reading Dependencies from Polytree-Like Bayesian Networks. UAI José M. Peña
2007	J	`jnl`	Towards scalable and data efficient learning of Markov boundaries. Int. J. Approx. Reason. José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2006	—	`conf`	Evaluating Feature Selection for SVMs in High Dimensions. ECML Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér
2006	—	`ch.`	GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm. Towards a New Evolutionary Computation Victor Robles, José M. Peña, Pedro Larrañaga, María S. Pérez, Vanessa Herves
2006	A	`conf`	Identifying the Relevant Nodes Without Learning the Model. UAI José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2006	—	`conf`	Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. Probabilistic Graphical Models José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér
2005	J	`jnl`	Editorial. Mach. Learn. Pedro Larrañaga, José Antonio Lozano, José M. Peña, Iñaki Inza
2005	J	`jnl`	Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. Evol. Comput. José M. Peña, José Antonio Lozano, Pedro Larrañaga
2005	—	`conf`	Growing Bayesian network models of gene networks from seed genes. ECCB/JBI José M. Peña, Johan Björkegren, Jesper Tegnér
2005	J	`jnl`	Learning dynamic Bayesian network models via cross-validation. Pattern Recognit. Lett. José M. Peña, Johan Björkegren, Jesper Tegnér
2005	C	`conf`	Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption. ECSQARU José M. Peña, Johan Björkegren, Jesper Tegnér
2004	J	`jnl`	Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering. Int. J. Uncertain. Fuzziness Knowl. Based Syst. José M. Peña, José Antonio Lozano, Pedro Larrañaga
2003	A	`conf`	On Local Optima in Learning Bayesian Networks. UAI Jens Dalgaard Nielsen, Tomás Kocka, José M. Peña
2002	—	`ch.`	Benefits of Data Clustering in Multimodal Function Optimization via EDAs. Estimation of Distribution Algorithms José M. Peña, José Antonio Lozano, Pedro Larrañaga
2002	J	`jnl`	Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. Mach. Learn. José M. Peña, José Antonio Lozano, Pedro Larrañaga
2002	—	`conf`	Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. Probabilistic Graphical Models José M. Peña, José Antonio Lozano, Pedro Larrañaga
2001	J	`jnl`	Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. IEEE Trans. Pattern Anal. Mach. Intell. José M. Peña, José Antonio Lozano, Pedro Larrañaga, Iñaki Inza
2001	A	`conf`	Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks. AISTATS José M. Peña, I. Izarzugaza, José Antonio Lozano, E. Aldasoro, Pedro Larrañaga
2001	J	`jnl`	Performance evaluation of compromise conditional Gaussian networks for data clustering. Int. J. Approx. Reason. José M. Peña, José Antonio Lozano, Pedro Larrañaga
2000	J	`jnl`	An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. Pattern Recognit. Lett. José M. Peña, José Antonio Lozano, Pedro Larrañaga
2000	A	`conf`	Combinatonal Optimization by Learning and Simulation of Bayesian Networks. UAI Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña
1999	J	`jnl`	An empirical comparison of four initialization methods for the K-Means algorithm. Pattern Recognit. Lett. José M. Peña, José Antonio Lozano, Pedro Larrañaga
1999	J	`jnl`	Learning Bayesian networks for clustering by means of constructive induction. Pattern Recognit. Lett. José M. Peña, José Antonio Lozano, Pedro Larrañaga
1999	J	`jnl`	Representing the behaviour of supervised classification learning algorithms by Bayesian networks. Pattern Recognit. Lett. Iñaki Inza, Pedro Larrañaga, Basilio Sierra, Ramon Etxeberria, José Antonio Lozano, José M. Peña