Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJosé M. Peña 0001
José Manuel Peña 0001 – Jose M. Peña 0001
Person information
- affiliation: Linköping University, Department of Computer and Information Science
- affiliation: Aalborg University, Department of Computer Science
- affiliation: University of the Basque Country, San Sebastián, Department of Computer Science and Artificial Intelligence
Other persons with the same name
- José M. Peña 0002 (aka: José-María Peña, José María Peña Sánchez) — Technical University of Madrid (UPM), CeSViMa
- José M. Peña 0003
![[0000-0003-4592-3792]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-4592-3792")
(aka: José Manuel Peña 0002, José Manuel Peñá-Barragán) — Spanish National Research Council, Institute of Agricultural Sciences, Madrid, Spain (and 1 more)
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[i31]Sourabh Balgi, Adel Daoud, José M. Peña, Geoffrey T. Wodtke, Jesse Zhou:
Deep Learning With DAGs. CoRR abs/2401.06864 (2024)- 2023
- [c32]Jose M. Peña:
Factorization of the Partial Covariance in Singly-Connected Path Diagrams. CLeaR 2023: 814-849 - [i30]Jose M. Peña:
Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies. CoRR abs/2303.05396 (2023) - [i29]Jose M. Peña:
On the Probability of Immunity. CoRR abs/2309.11942 (2023) - 2022
- [c31]Sourabh Balgi, José M. Peña, Adel Daoud:
Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows. AAAI 2022: 11810-11818 - [i28]Sourabh Balgi, Jose M. Peña, Adel Daoud:
Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flows. CoRR abs/2202.03281 (2022) - [i27]Sourabh Balgi, Jose M. Peña, Adel Daoud:
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows. CoRR abs/2202.09391 (2022) - [i26]Sourabh Balgi, José M. Peña, Adel Daoud:
ρ-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders. CoRR abs/2209.07111 (2022) - 2021
- [i25]Jose M. Peña:
On the Non-Monotonicity of a Non-Differentially Mismeasured Binary Confounder. CoRR abs/2101.08007 (2021) - [i24]Jose M. Peña:
Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding. CoRR abs/2104.13020 (2021) - 2020
- [i23]Jose M. Peña:
Conditional Path Analysis in Singly-Connected Path Diagrams. CoRR abs/2002.05226 (2020) - [i22]Jose M. Peña:
On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder. CoRR abs/2005.13245 (2020)
2010 – 2019
- 2018
- [c30]Jose M. Peña:
Unifying DAGs and UGs. PGM 2018: 308-319 - [c29]Jose M. Peña:
Identification of Strong Edges in AMP Chain Graphs. UAI 2018: 33-42 - [i21]Jose M. Peña:
Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances. CoRR abs/1806.08156 (2018) - [i20]Jose M. Peña:
Unifying Gaussian LWF and AMP Chain Graphs to Model Interference. CoRR abs/1811.04477 (2018) - 2017
- [j30]José M. Peña:
Representing independence models with elementary triplets. Int. J. Approx. Reason. 88: 587-601 (2017) - [j29]José M. Peña, Marcus Bendtsen:
Causal effect identification in acyclic directed mixed graphs and gated models. Int. J. Approx. Reason. 90: 56-75 (2017) - [c28]Jose M. Peña:
Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs. AMBN 2017: 21-32 - [c27]Jose M. Peña:
Learning Causal AMP Chain Graphs. AMBN 2017: 33-44 - [c26]Marcus Bendtsen, José M. Peña:
Modelling regimes with Bayesian network mixtures. SAIS 2017: 137:002 - [i19]Jose M. Peña:
Unifying DAGs and UGs. CoRR abs/1708.08722 (2017) - [i18]Jose M. Peña:
Identification of Strong Edges in AMP Chain Graphs. CoRR abs/1711.09990 (2017) - 2016
- [j28]Dag Sonntag, José M. Peña:
On expressiveness of the chain graph interpretations. Int. J. Approx. Reason. 68: 91-107 (2016) - [j27]José M. Peña, Manuel Gómez-Olmedo:
Learning marginal AMP chain graphs under faithfulness revisited. Int. J. Approx. Reason. 68: 108-126 (2016) - [j26]Marcus Bendtsen, José M. Peña:
Gated Bayesian networks for algorithmic trading. Int. J. Approx. Reason. 69: 58-80 (2016) - [c25]José M. Peña:
Learning Acyclic Directed Mixed Graphs from Observations and Interventions. Probabilistic Graphical Models 2016: 392-402 - [c24]José M. Peña:
Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs. UAI 2016 - [i17]Jose M. Peña:
Representing Independence Models with Elementary Triplets. CoRR abs/1612.01095 (2016) - [i16]José M. Peña, Marcus Bendtsen:
Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models. CoRR abs/1612.07512 (2016) - 2015
- [j25]Dag Sonntag, José M. Peña:
Chain graph interpretations and their relations revisited. Int. J. Approx. Reason. 58: 39-56 (2015) - [j24]José M. Peña:
Corrigendum to "Marginal AMP chain graphs" [Int. J. Approx. Reason. 55 (5) (2014) 1185-1206]. Int. J. Approx. Reason. 66: 139-140 (2015) - [j23]Dag Sonntag, José M. Peña, Manuel Gómez-Olmedo:
Approximate Counting of Graphical Models via MCMC Revisited. Int. J. Intell. Syst. 30(3): 384-420 (2015) - [c23]José M. Peña:
Every LWF and AMP Chain Graph Originates from a Set of Causal Models. ECSQARU 2015: 325-334 - [c22]José M. Peña:
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. ECSQARU 2015: 335-345 - [c21]Dag Sonntag, Matti Järvisalo, José M. Peña, Antti Hyttinen:
Learning Optimal Chain Graphs with Answer Set Programming. UAI 2015: 822-831 - [p3]Dag Sonntag, José M. Peña:
Chain Graphs and Gene Networks. Foundations of Biomedical Knowledge Representation 2015: 159-178 - [i15]José M. Peña:
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. CoRR abs/1501.06727 (2015) - [i14]José M. Peña:
Alternative Markov Properties for Acyclic Directed Mixed Graphs. CoRR abs/1511.05835 (2015) - 2014
- [j22]José M. Peña:
Learning AMP chain graphs and some marginal models thereof under faithfulness. Int. J. Approx. Reason. 55(4): 1011-1021 (2014) - [j21]José M. Peña:
Marginal AMP chain graphs. Int. J. Approx. Reason. 55(5): 1185-1206 (2014) - [c20]José M. Peña, Dag Sonntag, Jens Dalgaard Nielsen:
An inclusion optimal algorithm for chain graph structure learning. AISTATS 2014: 778-786 - [c19]Marcus Bendtsen, José M. Peña:
Learning Gated Bayesian Networks for Algorithmic Trading. Probabilistic Graphical Models 2014: 49-64 - [c18]José M. Peña:
Learning Marginal AMP Chain Graphs under Faithfulness. Probabilistic Graphical Models 2014: 382-395 - 2013
- [j20]José M. Peña:
Reading dependencies from covariance graphs. Int. J. Approx. Reason. 54(1): 216-227 (2013) - [j19]Kobra Etminani, Mahmoud Naghibzadeh, José M. Peña:
DemocraticOP: A Democratic way of aggregating Bayesian network parameters. Int. J. Approx. Reason. 54(5): 602-614 (2013) - [c17]José M. Peña:
Approximate Counting of Graphical Models via MCMC Revisited. CAEPIA 2013: 383-392 - [c16]Dag Sonntag, José M. Peña:
Chain Graph Interpretations and Their Relations. ECSQARU 2013: 510-521 - [c15]Marcus Bendtsen, José M. Peña:
Gated Bayesian Networks. SCAI 2013: 35-44 - [c14]José M. Peña:
Error AMP Chain Graphs. SCAI 2013: 215-224 - [i13]Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña:
Combinatorial Optimization by Learning and Simulation of Bayesian Networks. CoRR abs/1301.3871 (2013) - [i12]José M. Peña:
Approximate Counting of Graphical Models Via MCMC Revisited. CoRR abs/1301.7189 (2013) - [i11]José M. Peña:
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness. CoRR abs/1303.0691 (2013) - [i10]José M. Peña:
Marginal AMP Chain Graphs. CoRR abs/1305.0751 (2013) - [i9]José M. Peña:
Error AMP Chain Graphs. CoRR abs/1306.6843 (2013) - 2012
- [i8]José M. Peña:
Learning AMP Chain Graphs under Faithfulness. CoRR abs/1204.5357 (2012) - [i7]José M. Peña:
Reading Dependencies from Polytree-Like Bayesian Networks. CoRR abs/1206.5263 (2012) - [i6]José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
Identifying the Relevant Nodes Without Learning the Model. CoRR abs/1206.6847 (2012) - [i5]Jens Dalgaard Nielsen, Tomas Kocka, José M. Peña:
On Local Optima in Learning Bayesian Networks. CoRR abs/1212.2500 (2012) - 2011
- [j18]José M. Peña:
Finding Consensus Bayesian Network Structures. J. Artif. Intell. Res. 42: 661-687 (2011) - [c13]José M. Peña:
Faithfulness in Chain Graphs: The Gaussian Case. AISTATS 2011: 588-599 - [i4]José M. Peña:
A Correction of "Deriving a Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes". CoRR abs/1101.1715 (2011) - [i3]José M. Peña:
Towards Optimal Learning of Chain Graphs. CoRR abs/1109.5404 (2011) - 2010
- [j17]José M. Peña, Roland Nilsson:
On the Complexity of Discrete Feature Selection for Optimal Classification. IEEE Trans. Pattern Anal. Mach. Intell. 32(8): 1517-1522 (2010) - [i2]José M. Peña:
Faithfulness in Chain Graphs: The Gaussian Case. CoRR abs/1008.2277 (2010) - [i1]José M. Peña:
Reading Dependencies from Covariance Graphs. CoRR abs/1010.4504 (2010)
2000 – 2009
- 2009
- [j16]José M. Peña:
Faithfulness in chain graphs: The discrete case. Int. J. Approx. Reason. 50(8): 1306-1313 (2009) - [j15]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: 1071-1094 (2009) - 2008
- [c12]José M. Peña:
Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control. EvoBIO 2008: 165-176 - 2007
- [j14]Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér:
Detecting multivariate differentially expressed genes. BMC Bioinform. 8 (2007) - [j13]José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
Towards scalable and data efficient learning of Markov boundaries. Int. J. Approx. Reason. 45(2): 211-232 (2007) - [j12]Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér:
Consistent Feature Selection for Pattern Recognition in Polynomial Time. J. Mach. Learn. Res. 8: 589-612 (2007) - [c11]José M. Peña:
Reading Dependencies from Polytree-Like Bayesian Networks. UAI 2007: 303-309 - [c10]José M. Peña:
Approximate Counting of Graphical Models Via MCMC. AISTATS 2007: 355-362 - 2006
- [c9]Roland Nilsson, José M. Peña, Johan Björkegren, Jesper Tegnér:
Evaluating Feature Selection for SVMs in High Dimensions. ECML 2006: 719-726 - [c8]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: 247-254 - [c7]José M. Peña, Roland Nilsson, Johan Björkegren, Jesper Tegnér:
Identifying the Relevant Nodes Without Learning the Model. UAI 2006 - [p2]Victor Robles, José M. Peña, Pedro Larrañaga, María S. Pérez, Vanessa Herves:
GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm. Towards a New Evolutionary Computation 2006: 187-219 - 2005
- [j11]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): 43-66 (2005) - [j10]Pedro Larrañaga, José Antonio Lozano, José M. Peña, Iñaki Inza:
Editorial. Mach. Learn. 59(3): 211-212 (2005) - [j9]José M. Peña, Johan Björkegren, Jesper Tegnér:
Learning dynamic Bayesian network models via cross-validation. Pattern Recognit. Lett. 26(14): 2295-2308 (2005) - [c6]José M. Peña, Johan Björkegren, Jesper Tegnér:
Growing Bayesian network models of gene networks from seed genes. ECCB/JBI 2005: 229 - [c5]José M. Peña, Johan Björkegren, Jesper Tegnér:
Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption. ECSQARU 2005: 136-147 - 2004
- [j8]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): 63-82 (2004) - 2003
- [c4]Jens Dalgaard Nielsen, Tomás Kocka, José M. Peña:
On Local Optima in Learning Bayesian Networks. UAI 2003: 435-442 - 2002
- [j7]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. Mach. Learn. 47(1): 63-89 (2002) - [c3]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. Probabilistic Graphical Models 2002 - [p1]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Benefits of Data Clustering in Multimodal Function Optimization via EDAs. Estimation of Distribution Algorithms 2002: 101-127 - 2001
- [j6]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Performance evaluation of compromise conditional Gaussian networks for data clustering. Int. J. Approx. Reason. 28(1): 23-50 (2001) - [j5]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): 590-603 (2001) - [c2]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 - 2000
- [j4]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): 779-786 (2000) - [c1]Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña:
Combinatonal Optimization by Learning and Simulation of Bayesian Networks. UAI 2000: 343-352
1990 – 1999
- 1999
- [j3]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): 1027-1040 (1999) - [j2]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): 1201-1209 (1999) - [j1]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Learning Bayesian networks for clustering by means of constructive induction. Pattern Recognit. Lett. 20(11-13): 1219-1230 (1999)
Coauthor Index
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-02-08 23:13 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
