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Dhr. 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

    2020

    2019

    2018

    • Blom, T., Klimovskaia, A., Magliacane, S., & Mooij, J. M. (2018). An Upper Bound for Random Measurement Error in Causal Discovery. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 570-579). Corvallis, Oregon: AUAI Press. [details]
    • Forré, P., & Mooij, J. M. (2018). Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 269-278). Corvallis, Oregon: AUAI Press. [details]
    • Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., & Welling, M. (2018). Causal Effect Inference with Deep Latent-Variable Models. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 10, pp. 6447-6457). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/2017/file/94b5bde6de888ddf9cde6748ad2523d1-Paper.pdf [details]
    • Rubenstein, P. K., Bongers, S., Schölkopf, B., & Mooij, J. M. (2018). From Deterministic ODEs to Dynamic Structural Causal Models. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 114-123). [43] Corvallis, Oregon: AUAI Press. [details]

    2017

    • Magliacane, S., Claassen, T., & Mooij, J. (2017). Ancestral Causal Inference. In D. D. Lee, U. von Luxburg, R. Garnett, M. Sugiyama, & I. Guyon (Eds.), 30th Annual Conference on Neural Information Processing Systems 2016: Barcelona, Spain, 5-10 December 2016 (Vol. 7, pp. 4473-4481). (Advances in Neural Information Processing Systems; Vol. 29). Red Hook, NY: Curran Associates. [details]
    • Rubenstein, P. K., Weichwald, S., Bongers, S., Mooij, J. M., Janzing, D., Grosse-Wentrup, M., & Schölkopf, B. (2017). Causal Consistency of Structural Equation Models. In G. Elidan, & K. Kersting (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Third Conference (2017) : 11-15 August 2017, Sydney, Australia [11] Corvallis, OR: AUAI Press. [details]
    • van Ommen, T., & Mooij, J. M. (2017). Algebraic Equivalence of Linear Structural Equation Models. In G. Elidan, & K. Kersting (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Third Conference (2017) : 11-15 August 2017, Sydney, Australia [277] Corvallis, OR: AUAI Press. [details]

    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]

    2017

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

    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.
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