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Arjen Hommersom

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20142020

Research activity per year

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  • 2020

    Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

    Tummers, S. C. M. W., Hommersom, A. J., Lechner, E. H. S., Bolman, C. A. & Bemelmans, R., 2020, BNAIC/BeneLearn 2020 : Proceedings. Cao, L., Kosters, W. & Lijffijt, J. (eds.). Leiden: Leiden University, p. 298- 312 15 p.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

    Open Access
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  • Temporal Exceptional Model Mining Using Dynamic Bayesian Networks

    Bueno, M. L. P., Hommersom, A. J. & Lucas, P. J. F., 2020, Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020. Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R. & Ifrim, G. (eds.). Springer International Publishing AG, p. 97-112 16 p. (Lecture Notes in Computer Science (LNCS) series).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • 2019

    A Data-Driven Exploration of Hypotheses on Disease Dynamics

    Bueno, M. L. P., Hommersom, A., Lucas, P. J. F. & Janzing, J., 30 May 2019, Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings. Riaño, D., Wilk, S. & ten Teije, A. (eds.). Cham: Springer International Publishing AG, p. 170-179 10 p. (Lecture Notes in Computer Science (LNCS), Vol. 11526).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • 2018

    Denial-of-Service Attacks on LoRaWAN

    van Es, E., Vranken, H. & Hommersom, A., 2018, Proceedings of the 13th International Conference on Availability, Reliability and Security. New York, NY, USA: acm, 17

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • Discovering Software Vulnerabilities Using Data-flow Analysis and Machine Learning

    Kronjee, J., Hommersom, A. & Vranken, H., 2018, Proceedings of the 13th International Conference on Availability, Reliability and Security. New York, NY, USA: acm, 10 p. 6

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • Explaining the Most Probable Explanation

    Butz, R., Hommersom, A. & van Eekelen, M., 2018, Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. Ciucci, D., Pasi, G. & Vantaggi, B. (eds.). Cham: Springer International Publishing AG, p. 50-63 14 p. (Lecture Notes in Computer Science, Vol. 11142).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • Making Continuous Time Bayesian Networks More Flexible

    Liu, M., Stella, F., Hommersom, A. & Lucas, P. J. F., 1 Nov 2018, Proceedings of the Ninth International Conference on Probabilistic Graphical Models. Kratochvíl, V. & Studený, M. (eds.). Prague, Czech Republic: PMLR, Vol. 72. p. 237-248 12 p. (Proceedings of Machine Learning Research).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

    Open Access
  • Modeling the Dynamics of Multiple Disease Occurrence by Latent States

    Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Lobo, M. & Rodrigues, P. P., 2018, Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings. Ciucci, D., Pasi, G. & Vantaggi, B. (eds.). Cham: Springer International Publishing AG, p. 93-107 15 p. (Lecture Notes in Computer Science; No. 11142).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • Representing Hypoexponential Distributions in Continuous Time Bayesian Networks

    Liu, M., Stella, F., Hommersom, A. & Lucas, P. J. F., 2018, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. Medina, J., Ojeda-Aciego, M., Verdegay, J. L., Perfilieva, I., Bouchon-Meunier, B. & Yager, R. R. (eds.). Cham: Springer International Publishing AG, Vol. 855. p. 565-577 13 p. (Communications in Computer and Information Science, Vol. 855).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • 2017

    A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks

    Rabinowicz, S., Hommersom, A., Butz, R. & Williams, M., 2017, Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings. ten Teije, A., Popow, C., Holmes, J. H. & Sacchi, L. (eds.). Cham: Springer International Publishing AG, p. 81-85 5 p. (Lecture Notes in Computer Science, Vol. 10259).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • 2016

    Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming

    Michels, S., Hommersom, A. & Lucas, P. J. F., Jul 2016, IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Brewka, G. (ed.). AAAI Press, p. 3616-3622 7 p.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: an Industrial Case

    Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Verwer, S. & Linard, A., 2016, Proceedings of the Eighth International Conference on Probabilistic Graphical Models: Volume 52 of the JMLR Workshop and Conference Proceedings: PGM 2016, Lugano, 6–9 September 2016. Antonucci, A., Corani, G. & Campos, C. P. (eds.). PMLR, Vol. 52. p. 50-61 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

    Open Access
  • Learning Parameters of Hybrid Time Bayesian Networks

    Liu, M., Hommersom, A., Heijden, M. V. D. & Lucas, P. J. F., 2016, Proceedings of the Eighth International Conference on Probabilistic Graphical Models: Volume 52 of the JMLR Workshop and Conference Proceedings: PGM 2016, Lugano, 6–9 September 201. Antonucci, A., Corani, G. & de Campos, C. P. (eds.). PMLR, Vol. 52. p. 287-298 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

    Open Access
  • 2015

    Hybrid Time Bayesian Networks

    Liu, M., Hommersom, A. J., van der Heijden, M. & Lucas, P. J. F., 2015, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings. Destercke, S. & Denoeux, T. (eds.). Cham: Springer International Publishing AG, p. 376-386 11 p. (Lecture Notes in Computer Science (LNCS) series, Vol. 9161). (Lecture Notes in Artificial Intelligence (subseries), Vol. 9161).

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

  • Mining Hierarchical Pathology Data Using Inductive Logic Programming

    Op De Beéck, T., Hommersom, A., Van Haaren, J., van der Heijden, M., Davis, J., Lucas, P. J. F., Overbeek, L. & Nagtegaal, I., 2015, Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings. Holmes, J. H., Bellazzi, R., Sacchi, L. & Peek, N. (eds.). Cham: Springer International Publishing AG, p. 76-85 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review