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Feng, F., Huang, B., Zhang, K., & Magliacane, S. (2022). Factored Adaptation for Non-Stationary Reinforcement Learning. In NeurIPS Neural Information Processing Systems Foundation. https://arxiv.org/abs/2203.16582
Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, S. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 13557-13603). (Proceedings of Machine Learning Research). PMLR. https://proceedings.mlr.press/v162/lippe22a.html
2021
Hunt, N., Fulton, N., Magliacane, S., Hoang, T. N., Das, S., & Solar-Lezama, A. (2021). Verifiably Safe Exploration for End-to-End Reinforcement Learning. In HSCC2021: proceedings of the 24th International Conference on Hybrid Systems: Computation and Control (part of CPS-IoT Week) : May 19-21, 2021, Nashville, TN, USA [14] The Association for Computing Machinery. https://doi.org/10.1145/3447928.3456653[details]
Li, X., Magliacane, S., & Groth, P. (2021). The Challenges of Cross-Document Coreference Resolution in Email. In K-CAP '21: Proceedings of the 11th Knowledge Capture Conference : December 2-3, 2021 : virtual event, USA (pp. 273-276). Association for Computing Machinery. https://doi.org/10.1145/3460210.3493573[details]
Mooij, J. M., Magliacane, S., & Claassen, T. (2020). Joint Causal Inference from Multiple Contexts. Journal of Machine Learning Research, 21(99), [99]. https://www.jmlr.org/papers/v21/[details]
Magliacane, S., van Ommen, T., Claassen, T., Bongers, S., Versteeg, P., & Mooij, J. M. (2019). Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems 2018: Montreal, Canada, 3-8 December 2018 (Vol. 15, pp. 10846-10856). (Advances in Neural Information Processing Systems; Vol. 31). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/8282-domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions[details]
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). AUAI Press. http://auai.org/uai2018/proceedings/papers/208.pdf[details]
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). Curran Associates. http://papers.nips.cc/paper/6266-ancestral-causal-inference[details]
Hoekstra, R., Magliacane, S., Rietveld, L., de Vries, G., Wibisono, A., & Schlobach, S. (2015). Hubble: Linked Data Hub for Clinical Decision Support. In E. Simperl, B. Norton, D. Mladenic, E. Della Valle, I. Fundulaki, A. Passant, & R. Troncy (Eds.), The Semantic Web: ESWC 2012 Satellite Events: ESWC 2012 Satellite Events, Heraklion, Crete, Greece, May 27-31, 2012 : revised selected papers (pp. 458-462). (Lecture Notes in Computer Science; Vol. 7540). Springer. https://doi.org/10.1007/978-3-662-46641-4_45[details]
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