IDA - Department of Computer and Information Science

Jose M. Pe�a PhD in computer science, docent i datalogi Senior associate professor in computer science (bitr�dande professor i datalogi) Head of machine learning at the division of statistics and machine learning Department of computer and information science Link�ping university 58183 Link�ping, Sweden jose DOT m DOT pena AT liu DOT se B house, entrance 29B, room 2A:464

• Research
  
  I am interested in machine learning, artificial intelligence, and statistics. I do research on graphical models and causality.
  
  
• Teaching
  
  I teach courses on machine learning and causal inference.
  
  
• Publications
  
  • Pe�a, J. M. (2025). Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding. In Proceedings of the 4th Conference on Causal Learning and Reasoning (CLeaR 2025), to appear.
  • Balgi, S., Daoud, A., Pe�a, J. M., Wodtke, G. T., and Zhou, J. (2025). Deep Learning With DAGs. Sociological Methods and Research, to appear.
  • Pe�a, J. M. (2024). Alternative Measures of Direct and Indirect Effects. In Proceedings of the 12th International Conference on Probabilistic Graphical Models (PGM 2024) - Proceedings of Machine Learning Research 246, 1-19.
  • Balgi, S., Pe�a, J. M., and Daoud, A. (2024). ρ-GNF : A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows. In Proceedings of the 12th International Conference on Probabilistic Graphical Models (PGM 2024) - Proceedings of Machine Learning Research 246, 20-37. Student paper honourable mention.
  • Balgi, S., Pe�a, J. M., and Daoud, A. (2024). Counterfactually-Equivalent Structural Causal Modelling Using Causal Graphical Normalizing Flows. In Proceedings of the 12th International Conference on Probabilistic Graphical Models (PGM 2024) - Proceedings of Machine Learning Research 246, 164-181.
  • Pe�a, J. M. (2024). Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding. arXiv:2406.07940 [stat.ME].
  • Pe�a, J. M. (2023). On the Probability of Immunity. arXiv:2309.11942 [stat.ME].
  • Pe�a, J. M. (2023). Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies. Journal of Causal Inference, 11:20230012.
  • Pe�a, J. M. (2023). Factorization of the Partial Covariance in Singly-Connected Path Diagrams. In Proceedings of the 2nd Conference on Causal Learning and Reasoning (CLeaR 2023) - Proceedings of Machine Learning Research 213, 814-849.
  • Gabriel, E. E., Pe�a, J. M., and Sj�lander, A. (2022). Bias Attenuation Results for Dichotomization of a Continuous Confounder. Journal of Causal Inference, 10:515-526.
  • Sj�lander, A., Pe�a, J. M., and Gabriel, E. E. (2022). Bias Results for Nondifferential Mismeasurement of a Binary Confounder. Statistics & Probability Letters, 186:109474.
  • Pe�a, J. M. (2022). Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding. Journal of Causal Inference, 10:1-17.
  • Balgi, S., Pe�a, J. M., and Daoud, A. (2022). Personalized Public Policy Analysis In Social Sciences Using Causal-Graphical Normalizing Flows. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 11810�11818.
  • Pe�a, J. M., Balgi, S., Sj�lander, A. and Gabriel, E. E. (2021). On the Bias of Adjusting for a Non-Differentially Mismeasured Discrete Confounder. Journal of Causal Inference, 9:229-249.
  • Pe�a, J. M. (2020). On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder. Journal of Causal Inference, 8:150-163.
  • Pe�a, J. M. (2020). Unifying Gaussian LWF and AMP Chain Graphs to Model Interference. Journal of Causal Inference, 8:1-21.
  • Pe�a, J. M. (2018). Unifying DAGs and UGs. In Proceedings of the 9th International Conference on Probabilistic Graphical Models (PGM 2018) - Proceedings of Machine Learning Research 72, 308-319.
  • Pe�a, J. M. (2018). Identification of Strong Edges in AMP Chain Graphs. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI 2018).
  • Pe�a, J. M. (2018). Reasoning with Alternative Acyclic Directed Mixed Graphs. Behaviormetrika, 45:389-422.
  • Pe�a, J. M. (2017). Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs. In Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks (AMBN 2017) - Proceedings of Machine Learning Research 73, 21-32. Supplement.
  • Pe�a, J. M. (2017). Learning Causal AMP Chain Graphs. In Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks (AMBN 2017) - Proceedings of Machine Learning Research 73, 33-44.
  • Bendtsen, M. and Pe�a, J. M. (2017). Modelling Regimes with Bayesian Network Mixtures. In Proceedings of the 30th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS 2017), 20-29. Supplement.
  • Pe�a, J. M. and Bendtsen, M. (2017). Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models. International Journal of Approximate Reasoning, 90:56-75.
  • Pe�a, J. M. (2017). Representing Independence Models with Elementary Triplets. International Journal of Approximate Reasoning, 88:587-601.
  • Pe�a, J. M. (2016). Learning Acyclic Directed Mixed Graphs from Observations and Interventions. In Proceedings of the 8th International Conference on Probabilistic Graphical Models (PGM 2016) - Proceedings of Machine Learning Research 52, 392-402.
  • Pe�a, J. M. (2016). Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs. In Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 577-586. Supplement. Errata.
  • Bendtsen, M. and Pe�a, J. M. (2016). Gated Bayesian Networks for Algorithmic Trading. International Journal of Approximate Reasoning, 69:58-80.
  • Pe�a, J. M. and G�mez-Olmedo, M. (2016). Learning Marginal AMP Chain Graphs under Faithfulness Revisited. International Journal of Approximate Reasoning, 68:108-126.
  • Sonntag, D. and Pe�a, J. M. (2016). On Expressiveness of the Chain Graph Interpretations. International Journal of Approximate Reasoning, 68:91-107.
  • Keivani, O. and Pe�a, J. M. (2016). Uni- and Multi-Dimensional Clustering Via Bayesian Networks. In Unsupervised Learning Algorithms, 163-192. Springer.
  • Pe�a, J. M. (2015). Representing Independence Models with Elementary Triplets. In Proceedings of the 10th Workshop on Uncertainty Processing (WUPES 2015), 155-166. Extended version.
  • Sonntag, D., J�rvisalo, M., Pe�a, J. M. and Hyttinen, A. (2015). Learning Optimal Chain Graphs with Answer Set Programming. In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), 822-831.
  • Pe�a, J. M. (2015). Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs. In Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning under Uncertainty (ECSQARU 2015) - Lecture Notes in Artificial Intelligence 9161, 335-345. Addendum.
  • Pe�a, J. M. (2015). Every LWF and AMP Chain Graph Originates from a Set of Causal Models. In Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning under Uncertainty (ECSQARU 2015) - Lecture Notes in Artificial Intelligence 9161, 325-334.
  • Sonntag, D., Pe�a, J. M. and G�mez-Olmedo, M. (2015). Approximate Counting of Graphical Models Via MCMC Revisited. International Journal of Intelligent Systems, 30:384-420.
  • Sonntag, D. and Pe�a, J. M. (2015). Chain Graph Interpretations and their Relations Revisited. International Journal of Approximate Reasoning, 58:39-56.
  • Sonntag, D. and Pe�a, J. M. (2015). Chain Graphs and Gene Networks. In Foundations of Biomedical Knowledge Representation, 159-178. Springer.
  • Pe�a, J. M. (2014). Learning Marginal AMP Chain Graphs under Faithfulness. In Proceedings of the 7th European Workshop on Probabilistic Graphical Models (PGM 2014) - Lecture Notes in Artificial Intelligence 8754, 382-395.
  • Bendtsen, M. and Pe�a, J. M. (2014). Learning Gated Bayesian Networks for Algorithmic Trading. In Proceedings of the 7th European Workshop on Probabilistic Graphical Models (PGM 2014) - Lecture Notes in Artificial Intelligence 8754, 49-64.
  • Pe�a, J. M. (2014). Marginal AMP Chain Graphs. International Journal of Approximate Reasoning, 55:1185-1206. Errata. Corrected article.
  • Pe�a, J. M., Sonntag, D. and Nielsen, J. D. (2014). An Inclusion Optimal Algorithm for Chain Graph Structure Learning. In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014), 778-786. Supplement.
  • Pe�a, J. M. (2014). Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness. International Journal of Approximate Reasoning, 55:1011-1021. Extended version. Addendum.
  • Bendtsen, M. and Pe�a, J. M. (2013). Gated Bayesian Networks. In Proceedings of the 12th Scandinavian Conference on Artificial Intelligence (SCAI 2013) - Frontiers in Artificial Intelligence and Applications 257, 35-44.
  • Pe�a, J. M. (2013). Error AMP Chain Graphs. In Proceedings of the 12th Scandinavian Conference on Artificial Intelligence (SCAI 2013) - Frontiers in Artificial Intelligence and Applications 257, 215-224. Extended version.
  • Sonntag, D. and Pe�a, 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 7958, 510-521. Best student paper award (ex-aequo). Extended version.
  • Pe�a, J. M. (2013). Approximate Counting of Graphical Models Via MCMC Revisited. In Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2013) - Lecture Notes in Artificial Intelligence 8109, 383-392. Best paper award. Software.
  • Etminani, K., Naghibzadeh, M. and Pe�a, J. M. (2013). DemocraticOP: A Democratic Way of Aggregating Bayesian Network Parameters. International Journal of Approximate Reasoning, 54:602-614.
  • Pe�a, J. M. (2013). Reading Dependencies from Covariance Graphs. International Journal of Approximate Reasoning, 54:216-227.
  • Pe�a, 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 Pe�a, 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. Addendum.
  • Pe�a, J. M. (2011). Finding Consensus Bayesian Network Structures. Journal of Artificial Intelligence Research, 42:661-687.
  • Pe�a, J. M. (2011). Faithfulness in Chain Graphs: The Gaussian Case. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011) - Proceedings of Machine Learning Research 15, 588-599, 2011.
  • Pe�a, 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.
  • Pe�a, J. M. and Nilsson, R. (2010). On the Complexity of Discrete Feature Selection for Optimal Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32:1517-1522.
  • Pe�a, J. M. (2009). Faithfulness in Chain Graphs: The Discrete Case. International Journal of Approximate Reasoning, 50:1306-1313.
  • Pe�a, J. M., Nilsson, R., Bj�rkegren, J. and Tegn�r, 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.
  • Pe�a, 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.
  • Pe�a, 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., Pe�a, J. M., Bj�rkegren, J. and Tegn�r, J. (2007). Detecting Multivariate Differentially Expressed Genes. BMC Bioinformatics, 8:150.
  • Nilsson, R., Pe�a, J. M., Bj�rkegren, J. and Tegn�r, J. (2007). Consistent Feature Selection for Pattern Recognition in Polynomial Time. Journal of Machine Learning Research, 8:589-612.
  • Pe�a, 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.
  • Pe�a, J. M., Bj�rkegren, J. and Tegn�r, J. (2007). Learning and Validating Bayesian Network Models of Gene Networks. In Advances in Probabilistic Graphical Models, 359-376. Springer.
  • Pe�a, J. M., Nilsson, R., Bj�rkegren, J. and Tegn�r, J. (2007). Towards Scalable and Data Efficient Learning of Markov Boundaries. International Journal of Approximate Reasoning, 45:211-232. Software.
  • Pe�a, J. M., Nilsson, R., Bj�rkegren, J. and Tegn�r, 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., Pe�a, J. M., Bj�rkegren, J. and Tegn�r, 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.
  • Pe�a, J. M., Nilsson, R., Bj�rkegren, J. and Tegn�r, 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.
  • Pe�a, J. M., Bj�rkegren, J. and Tegn�r, J. (2005). Growing Bayesian Network Models of Gene Networks from Seed Genes. Bioinformatics, 21:ii224-ii229. Software.
  • Pe�a, J. M., Bj�rkegren, J. and Tegn�r, J. (2005). Learning Dynamic Bayesian Network Models Via Cross-Validation. Pattern Recognition Letters, 26:2295-2308.
  • Pe�a, J. M., Bj�rkegren, J. and Tegn�r, 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.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, P. (2005). Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. Evolutionary Computation, 13:43-66.
  • Pe�a, 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.
  • Pe�a, J. M., Kočka, 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.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, 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, 12:63-82.
  • S�ndberg-Madsen, N., Thomsen, C. and Pe�a, 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., Kočka, T. and Pe�a, 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.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, 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.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, P. (2002). Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. Machine Learning, 47:63-89.
  • Pe�a, J. M. (2001). On Unsupervised Learning of Bayesian Networks and Conditional Gaussian Networks. PhD Thesis, University of the Basque Country, Spain.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, P. (2001). Performance Evaluation of Compromise Conditional Gaussian Networks for Data Clustering. International Journal of Approximate Reasoning, 28:23-50.
  • Pe�a, J. M., Lozano, J. A., Larra�aga, P. and Inza, I. (2001). Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:590-603.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, 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.
  • Pe�a, J. M., Izarzugaza, I., Lozano, J. A., Aldasoro, E. and Larra�aga, 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.
  • Larra�aga, P., Etxeberria, R., Lozano, J. A. and Pe�a, 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.
  • Larra�aga, P., Etxeberria, R., Lozano, J. A. and Pe�a, 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.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, P. (2000). An Improved Bayesian Structural EM Algorithm for Learning Bayesian Networks for Clustering. Pattern Recognition Letters, 21:779-786.
  • Larra�aga, P., Etxeberria, R., Lozano, J. A., Sierra, B., Inza, I. and Pe�a, 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., Larra�aga, P., Sierra, B., Etxeberria, R., Lozano, J. A. and Pe�a, J. M. (1999). Representing the Joint Behaviour of Machine Learning Inducers by Bayesian Networks. Pattern Recognition Letters, 20:1201-1209.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, P. (1999). Learning Bayesian Networks for Clustering by Means of Constructive Induction. Pattern Recognition Letters, 20:1219-1230.
  • Pe�a, J. M., Lozano, J. A. and Larra�aga, P. (1999). An Empirical Comparison of Four Initialization Methods for the K-Means Algorithm. Pattern Recognition Letters, 20:1027-1040.

Page responsible: Jose M. Pe�a
Last updated: 2025-01-29