[{"extern":"1","article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","keyword":["Electrical and Electronic Engineering"],"intvolume":"       109","date_created":"2023-08-21T12:19:30Z","department":[{"_id":"FrLo"}],"article_type":"original","volume":109,"month":"05","quality_controlled":"1","publication_identifier":{"eissn":["1558-2256"],"issn":["0018-9219"]},"type":"journal_article","language":[{"iso":"eng"}],"doi":"10.1109/jproc.2021.3058954","publisher":"Institute of Electrical and Electronics Engineers","year":"2021","date_published":"2021-05-01T00:00:00Z","author":[{"first_name":"Bernhard","full_name":"Scholkopf, Bernhard","last_name":"Scholkopf"},{"orcid":"0000-0002-4850-0683","last_name":"Locatello","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","first_name":"Francesco","full_name":"Locatello, Francesco"},{"last_name":"Bauer","first_name":"Stefan","full_name":"Bauer, Stefan"},{"last_name":"Ke","first_name":"Nan Rosemary","full_name":"Ke, Nan Rosemary"},{"last_name":"Kalchbrenner","first_name":"Nal","full_name":"Kalchbrenner, Nal"},{"last_name":"Goyal","first_name":"Anirudh","full_name":"Goyal, Anirudh"},{"full_name":"Bengio, Yoshua","first_name":"Yoshua","last_name":"Bengio"}],"oa":1,"_id":"14117","issue":"5","scopus_import":"1","citation":{"ieee":"B. Scholkopf <i>et al.</i>, “Toward causal representation learning,” <i>Proceedings of the IEEE</i>, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021.","chicago":"Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers, 2021. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>.","ama":"Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. <i>Proceedings of the IEEE</i>. 2021;109(5):612-634. doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>","ista":"Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634.","apa":"Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., &#38; Bengio, Y. (2021). Toward causal representation learning. <i>Proceedings of the IEEE</i>. Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/jproc.2021.3058954\">https://doi.org/10.1109/jproc.2021.3058954</a>","mla":"Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” <i>Proceedings of the IEEE</i>, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:<a href=\"https://doi.org/10.1109/jproc.2021.3058954\">10.1109/jproc.2021.3058954</a>.","short":"B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634."},"arxiv":1,"publication":"Proceedings of the IEEE","external_id":{"arxiv":["2102.11107"]},"title":"Toward causal representation learning","main_file_link":[{"url":"https://doi.org/10.1109/JPROC.2021.3058954","open_access":"1"}],"status":"public","date_updated":"2023-09-11T11:43:35Z","oa_version":"Published Version","page":"612-634","publication_status":"published","day":"01","abstract":[{"lang":"eng","text":"The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities."}]},{"user_id":"ea97e931-d5af-11eb-85d4-e6957dddbf17","extern":"1","article_processing_charge":"No","intvolume":"        88","date_created":"2018-12-11T12:09:41Z","acknowledgement":"The authors would like to thank the reviewers for their detailed comments.","article_type":"original","volume":88,"month":"07","publist_id":"107","publication_identifier":{"issn":["0018-9219"]},"quality_controlled":"1","type":"journal_article","doi":"10.1109/5.871304 ","language":[{"iso":"eng"}],"year":"2000","publisher":"IEEE","date_published":"2000-07-01T00:00:00Z","author":[{"last_name":"Alur","first_name":"Rajeev","full_name":"Alur, Rajeev"},{"first_name":"Thomas A","full_name":"Henzinger, Thomas A","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000−0002−2985−7724"},{"last_name":"Lafferriere","full_name":"Lafferriere, Gerardo","first_name":"Gerardo"},{"full_name":"Pappas, George","first_name":"George","last_name":"Pappas"}],"_id":"4598","scopus_import":"1","issue":"7","citation":{"ama":"Alur R, Henzinger TA, Lafferriere G, Pappas G. Discrete abstractions of hybrid systems. <i>Proceedings of the IEEE</i>. 2000;88(7):971-984. doi:<a href=\"https://doi.org/10.1109/5.871304 \">10.1109/5.871304 </a>","apa":"Alur, R., Henzinger, T. A., Lafferriere, G., &#38; Pappas, G. (2000). Discrete abstractions of hybrid systems. <i>Proceedings of the IEEE</i>. IEEE. <a href=\"https://doi.org/10.1109/5.871304 \">https://doi.org/10.1109/5.871304 </a>","short":"R. Alur, T.A. Henzinger, G. Lafferriere, G. Pappas, Proceedings of the IEEE 88 (2000) 971–984.","mla":"Alur, Rajeev, et al. “Discrete Abstractions of Hybrid Systems.” <i>Proceedings of the IEEE</i>, vol. 88, no. 7, IEEE, 2000, pp. 971–84, doi:<a href=\"https://doi.org/10.1109/5.871304 \">10.1109/5.871304 </a>.","ista":"Alur R, Henzinger TA, Lafferriere G, Pappas G. 2000. Discrete abstractions of hybrid systems. Proceedings of the IEEE. 88(7), 971–984.","ieee":"R. Alur, T. A. Henzinger, G. Lafferriere, and G. Pappas, “Discrete abstractions of hybrid systems,” <i>Proceedings of the IEEE</i>, vol. 88, no. 7. IEEE, pp. 971–984, 2000.","chicago":"Alur, Rajeev, Thomas A Henzinger, Gerardo Lafferriere, and George Pappas. “Discrete Abstractions of Hybrid Systems.” <i>Proceedings of the IEEE</i>. IEEE, 2000. <a href=\"https://doi.org/10.1109/5.871304 \">https://doi.org/10.1109/5.871304 </a>."},"publication":"Proceedings of the IEEE","title":"Discrete abstractions of hybrid systems","status":"public","date_updated":"2023-04-13T13:32:11Z","day":"01","page":"971 - 984","publication_status":"published","oa_version":"None","abstract":[{"text":"A hybrid system is a dynamical system with both discrete and continuous state changes. For analysis purposes, it is often useful to abstract a system in a way that preserves the properties being analyzed while hiding the details that are of no interest. We show that interesting classes of hybrid systems can be abstracted to purely discrete systems while preserving all properties that are definable in temporal logic. The classes that permit discrete abstractions fall into two categories. Either the continuous dynamics must be restricted, as is the case for timed and rectangular hybrid systems, or the discrete dynamics must be restricted, as is the case for o-minimal hybrid systems. In this paper, we survey and unify results from both areas.","lang":"eng"}]}]