Department of Computer and Information Science

Jose M. Peña PhD in CS, Docent i Datalogi Associate Professor in CS (Universitetslektor i Datalogi) Building B, Room 2B:444 Division for Database and Information Techniques Department of Computer and Information Science Linköping University 58183 Linköping, Sweden +46 13 281651 (office) +46 13 142231 (fax) jose.m.pena (Skype) jose.m.pena AT liu DOT se

• Curriculum vitae
  
  
• Research
  
  I am interested in learning from data within machine learning, artificial intelligence, and statistics. I am particularly interested in learning probabilistic graphical models such as Bayesian networks, Markov networks and chain graphs.
  
  
• Publications (under construction, old page here)
  
  • Sonntag, D. and Peña, J. M. (2013). Chain Graph Interpretations and their Relations. In Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning under Uncertainty (ECSQARU 2013) – Lecture Notes in Artificial Intelligence, to appear. Extended version.
  • Peña, J. M. (2013). Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness. arXiv:1303.0691[stat.ML].
    
  • Peña, J. M. (2013). Approximate Counting of Graphical Models Via MCMC Revisited. arXiv:1301.7189 [stat.ML]. Software.
    
  • Etminani, K., Naghibzadeh, M. and Peña, J. M. (2013). DemocraticOP: A Democratic Way of Aggregating Bayesian Network Parameters. International Journal of Approximate Reasoning, to appear.
    
  • Peña, J. M. (2013). Reading Dependencies from Covariance Graphs. International Journal of Approximate Reasoning, 54 (1), 216-227.
  • Peña, J. M. (2012). Learning AMP Chain Graphs under Faithfulness. In Proceedings of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012), 251-258.
    
  • Sonntag, D. and Peña, J. M. (2012). Learning Multivariate Regression Chain Graphs under Faithfulness. In Proceedings of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012), 299-306. Appendix.
  • Peña, J. M. (2011). Towards Optimal Learning of Chain Graphs. arXiv:1109.5404 [stat.ML].
  • Peña, J. M. (2011). Finding Consensus Bayesian Network Structures. Journal of Artificial Intelligence Research, 42, 661-687.
  • Peña, J. M. (2011). Faithfulness in Chain Graphs: The Gaussian Case. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), 588-599.
  • Peña, J. M. (2010). Reading Dependencies from Polytree-Like Bayesian Networks Revisited. In Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM 2010), 225-232.
  • Peña, J. M. and Nilsson, R. (2010). On the Complexity of Discrete Feature Selection for Optimal Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (8), 1517-1522.
  • Peña, J. M. (2009). Faithfulness in Chain Graphs: The Discrete Case. International Journal of Approximate Reasoning, 50 (8), 1306-1313.
  • Peña, J. M., Nilsson, R., Björkegren, J. and Tegnér, J. (2009). An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. Journal of Machine Learning Research, 10, 1071-1094.
  • Peña, J. M. (2008). Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control. In Proceedings of the 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2008) – Lectures Notes in Computer Science 4973, 165-176. Software.
  • Peña, J. M. (2007). Reading Dependencies from Polytree-Like Bayesian Networks. In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI 2007), 303-309. Errata.
  • Nilsson, R., Peña, J. M., Björkegren, J. and Tegnér, J. (2007). Detecting Multivariate Differentially Expressed Genes. BMC Bioinformatics, 8:150.
  • Nilsson, R., Peña, J. M., Björkegren, J. and Tegnér, J. (2007). Consistent Feature Selection for Pattern Recognition in Polynomial Time. Journal of Machine Learning Research, 8, 589-612.
  • Peña, J. M. (2007). Approximate Counting of Graphical Models Via MCMC. In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), 352-359. Software.
  • Peña, J. M., Björkegren, J. and Tegnér, J. (2007). Learning and Validating Bayesian Network Models of Gene Networks. In Advances in Probabilistic Graphical Models, 359-376. Springer.
  • Peña, J. M., Nilsson, R., Björkegren, J. and Tegnér, J. (2007). Towards Scalable and Data Efficient Learning of Markov Boundaries. International Journal of Approximate Reasoning, 45(2), 211-232. Software.
  • Peña, J. M., Nilsson, R., Björkegren, J. and Tegnér, J. (2006). Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. In Proceedings of the 3rd European Workshop on Probabilistic Graphical Models (PGM 2006), 247-254.
  • Nilsson, R., Peña, J. M., Björkegren, J. and Tegnér, J. (2006). Evaluating Feature Selection for SVMs in High Dimensions. In Proceedings of 17th European Conference on Machine Learning (ECML 2006) – Lectures Notes in Computer Science 4212, 719-726.
  • Peña, J. M., Nilsson, R., Björkegren, J. and Tegnér, J. (2006). Identifying the Relevant Nodes Without Learning the Model. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI 2006), 367-374.
  • Peña, J. M., Björkegren, J. and Tegnér, J. (2005). Growing Bayesian Network Models of Gene Networks from Seed Genes. Bioinformatics, 21 (Supplement 2), ii224-ii229. Software.
  • Peña, J. M., Björkegren, J. and Tegnér, J. (2005). Learning Dynamic Bayesian Network Models Via Cross-Validation. Pattern Recognition Letters, 26 (14), 2295-2308.
  • Peña, J. M., Björkegren, J. and Tegnér, J. (2005). Scalable, Efficient and Correct Learning of Markov Boundaries under the Faithfulness Assumption. In Proceedings of the 8th European Conference on Symbolic and Quantitative Approaches to Reasoning under Uncertainty (ECSQARU 2005) – Lecture Notes in Artificial Intelligence 3571, 136-147. Software.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (2005). Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. Evolutionary Computation, 13 (1), 43-66.
  • Peña, J. M. (2004). Learning and Validating Bayesian Network Models of Genetic Regulatory Networks. In Proceedings of the 2nd European Workshop on Probabilistic Graphical Models (PGM 2004), 161-168.
  • Peña, J. M., Kocka, T. and Nielsen, J. D. (2004). Featuring Multiple Local Optima to Assist the User in the Interpretation of Induced Bayesian Network Models. In Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), 1683-1690.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (2004). Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms: An Application to Gene Expression Data Clustering. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12 (1), 63-82.
  • Søndberg-Madsen, N., Thomsen, C. and Peña, J. M. (2003). Unsupervised Feature Subset Selection. In Proceedings of the Workshop on Probabilistic Graphical Models for Classification (within ECML 2003), 71-82.
  • Nielsen, J. D., Kocka, T. and Peña, J. M. (2003). On Local Optima in Learning Bayesian Networks. In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI 2003), 435-442.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (2002). Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. In Proceedings of the 1st European Workshop on Probabilistic Graphical Models (PGM 2002), 144-151.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (2002). Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. Machine Learning, 47 (1), 63-89.
  • Peña, J. M. (2001). On Unsupervised Learning of Bayesian Networks and Conditional Gaussian Networks. PhD Thesis, University of the Basque Country, Spain.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (2001). Performance Evaluation of Compromise Conditional Gaussian Networks for Data Clustering. International Journal of Approximate Reasoning, 28 (1), 23-50.
  • Peña, J. M., Lozano, J. A., Larrañaga, P. and Inza, I. (2001). Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (6), 590-603.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (2001). Benefits of Data Clustering in Multimodal Function Optimization via EDAs. In Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation, 101-127. Kluwer Academic Publishers.
  • Peña, J. M., Izarzugaza, I., Lozano, J. A., Aldasoro, E. and Larrañaga, P. (2001). Geographical Clustering of Cancer Incidence by Means of Bayesian Networks and Conditional Gaussian Networks. In Proceedings of the 8th International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), 266-271.
  • Larrañaga, P., Etxeberria, R., Lozano, J. A. and Peña, J. M. (2000). Combinatorial Optimization by Learning and Simulation of Bayesian Networks. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI 2000), 343-352.
  • Larrañaga, P., Etxeberria, R., Lozano, J. A. and Peña, J. M. (2000). Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks. In Proceedings of the Workshop in Optimization by Building and Using Probabilistic Models (within GECCO 2000), 201-204.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (2000). An Improved Bayesian Structural EM Algorithm for Learning Bayesian Networks for Clustering. Pattern Recognition Letters, 21 (8), 779-786.
  • Larrañaga, P., Etxeberria, R., Lozano, J. A., Sierra, B., Inza, I. and Peña, J. M. (1999). A Review of the Cooperation Between Evolutionary Computation and Probabilistic Graphical Models. In Proceedings of the 2nd Symposium on Artificial Intelligence, 314-324.
  • Inza, I., Larrañaga, P., Sierra, B., Etxeberria, R., Lozano, J. A. and Peña, J. M. (1999). Representing the Joint Behaviour of Machine Learning Inducers by Bayesian Networks. Pattern Recognition Letters, 20 (11-13), 1201-1209.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (1999). Learning Bayesian Networks for Clustering by Means of Constructive Induction. Pattern Recognition Letters, 20 (11-13), 1219-1230.
  • Peña, J. M., Lozano, J. A. and Larrañaga, P. (1999). An Empirical Comparison of Four Initialization Methods for the K-Means Algorithm. Pattern Recognition Letters, 20 (10), 1027-1040.

Page responsible: Jose M. Peña
Last updated: 2013-04-03