Voor de beste ervaring schakelt u JavaScript in en gebruikt u een moderne browser!
Je gebruikt een niet-ondersteunde browser. Deze site kan er anders uitzien dan je verwacht.

Prof. dr. J.M. (Joris) Mooij

Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Korteweg-de Vries Instituut

Bezoekadres
  • Science Park 107
Postadres
  • Postbus 94248
    1090 GE Amsterdam
Contactgegevens
  • Publicaties

    2021

    • Blom, T., Van Diepen, M., & Mooij, J. M. (2021). Conditional independences and causal relations implied by sets of equations. Journal of Machine Learning Research, 22(178), 1-62. https://jmlr.org/papers/volume22/20-863/20-863.pdf
    • Boeken, P. A., & Mooij, J. M. (2021). A Bayesian nonparametric conditional two-sample test with an application to Local Causal Discovery. Proceedings of Machine Learning Research, 161, 1565-1575.
    • Bongers, S., Forré, P., Peters, J., & Mooij, J. M. (2021). Foundations of structural causal models with cycles and latent variables. The Annals of Statistics, 49(5), 2885-2915. https://doi.org/10.1214/21-AOS2064 [details]
    • Marx, A., Gretton, A., & Mooij, J. M. (2021). A weaker faithfulness assumption based on triple interactions. Proceedings of Machine Learning Research, 161, 451-460.

    2020

    2019

    2018

    2017

    2016

    2015

    • Mooij, J. M., & Cremers, J. (2015). An Empirical Study of one of the Simplest Causal Prediction Algorithms. In R. Silva, I. Shpitser, R. Evans, J. Peters, & T. Claassen (Eds.), UAI2015-ACI : UAI 2015 Workshop on Advances in Causal Inference: Proceedings of the UAI 2015 Workshop on Advances in Causal Inference, co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015) : Amsterdam, The Netherlands, July 16, 2015 (pp. 30-39). [2] (CEUR Workshop Proceedings; Vol. 1504). CEUR-WS. http://ceur-ws.org/Vol-1504/uai2015aci_paper2.pdf [details]
    • de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015). MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Computational Biology, 11(4), [e004219]. https://doi.org/10.1371/journal.pcbi.1004219 [details]

    2014

    • Cornia, N., & Mooij, J. M. (2014). Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example. In J. M. Mooij, D. Janzing, J. Peters, T. Claassen, & A. Hyttinen (Eds.), UAI2014CI : UAI 2014 Workshop on Causal Inference: Learning and Prediction: Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction, co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014) : Quebec City, Canada, July 27, 2014 (pp. 35-42). (CEUR Workshop Proceedings; Vol. 1274). CEUR-WS. http://ceur-ws.org/Vol-1274/uai2014ci_paper7.pdf [details]
    • Peters, J., Mooij, J. M., Janzing, D., & Schölkopf, B. (2014). Causal Discovery with Continuous Additive Noise Models. Journal of Machine Learning Research, 15, 2009-2053. [details]

    2013

    • Claassen, T., Mooij, J. M., & Heskes, T. (2013). Learning sparse causal models is not NP-hard. In A. Nicholson, & P. Smyth (Eds.), Uncertainty in artificial intelligence: proceedings of the twenty-ninth conference (2013): July 12-14, 2013, Bellevue, Washington, United States (pp. 172-181). Corvallis, Oregon: AUAI Press. [details]
    • Mooij, J. M., & Heskes, T. (2013). Cyclic causal discovery from continuous equilibrium data. In A. Nicholson, & P. Smyth (Eds.), Uncertainty in artificial intelligence: proceedings of the twenty-ninth conference (2013): July 12-14, 2013, Bellevue, Washington, United States (pp. 431-439). Corvallis, Oregon: AUAI Press. [details]
    • Mooij, J. M., Janzing, D., & Schölkopf, B. (2013). From Ordinary Differential Equations to Structural Causal Models: the deterministic case. In A. Nicholson, & P. Smyth (Eds.), Uncertainty in artificial intelligence: proceedings of the twenty-ninth conference (2013): July 12-14, 2013, Bellevue, Washington, United States (pp. 440-448). Corvallis, Oregon: AUAI Press. [details]

    2014

    • Claassen, T., Mooij, J. M., & Heskes, T. (2014). Supplement - Learning Sparse Causal Models is not NP-hard. Ithaca, NY: arXiv.org. [details]
    • Mooij, J. M., Janzing, D., Peters, J., Claassen, T., & Hyttinen, A. (Eds.) (2014). UAI2014CI : UAI 2014 Workshop on Causal Inference: Learning and Prediction: Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction, co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014) : Quebec City, Canada, July 27, 2014. (CEUR Workshop Proceedings; Vol. 1274). CEUR-WS. http://ceur-ws.org/Vol-1274 [details]

    2022

    • Bongers, S. R. (2022). Causal modeling & dynamical systems: A new perspective on feedback. [details]
    • Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [details]

    2021

    • Blom, T. (2021). Causality and independence in systems of equations. [details]

    2017

    • Kingma, D. P. (2017). Variational inference & deep learning: A new synthesis. [details]

    2022

    2020

    2017

    • Forré, P., & Mooij, J. M. (2017). Markov Properties for Graphical Models with Cycles and Latent Variables. Amsterdam: Informatics Institute, University of Amsterdam. [details]
    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
  • Nevenwerkzaamheden
    • Geen nevenwerkzaamheden