{"year":"2019","intvolume":"       115","volume":115,"external_id":{"arxiv":["1905.06642"]},"author":[{"last_name":"Gresele","first_name":"Luigi","full_name":"Gresele, Luigi"},{"full_name":"Rubenstein, Paul K.","last_name":"Rubenstein","first_name":"Paul K."},{"last_name":"Mehrjou","first_name":"Arash","full_name":"Mehrjou, Arash"},{"last_name":"Locatello","first_name":"Francesco","full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","orcid":"0000-0002-4850-0683"},{"first_name":"Bernhard","last_name":"Schölkopf","full_name":"Schölkopf, Bernhard"}],"publication":"Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence","page":"217-227","_id":"14189","language":[{"iso":"eng"}],"conference":{"location":"Tel Aviv, Israel","end_date":"2019-07-25","name":"UAI: Uncertainty in Artificial Intelligence","start_date":"2019-07-22"},"citation":{"ista":"Gresele L, Rubenstein PK, Mehrjou A, Locatello F, Schölkopf B. 2019. The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA. Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence. UAI: Uncertainty in Artificial Intelligence, PMLR, vol. 115, 217–227.","apa":"Gresele, L., Rubenstein, P. K., Mehrjou, A., Locatello, F., &#38; Schölkopf, B. (2019). The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA. In <i>Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence</i> (Vol. 115, pp. 217–227). Tel Aviv, Israel: ML Research Press.","chicago":"Gresele, Luigi, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, and Bernhard Schölkopf. “The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA.” In <i>Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence</i>, 115:217–27. ML Research Press, 2019.","ama":"Gresele L, Rubenstein PK, Mehrjou A, Locatello F, Schölkopf B. The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA. In: <i>Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence</i>. Vol 115. ML Research Press; 2019:217-227.","short":"L. Gresele, P.K. Rubenstein, A. Mehrjou, F. Locatello, B. Schölkopf, in:, Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence, ML Research Press, 2019, pp. 217–227.","mla":"Gresele, Luigi, et al. “The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA.” <i>Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence</i>, vol. 115, ML Research Press, 2019, pp. 217–27.","ieee":"L. Gresele, P. K. Rubenstein, A. Mehrjou, F. Locatello, and B. Schölkopf, “The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA,” in <i>Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence</i>, Tel Aviv, Israel, 2019, vol. 115, pp. 217–227."},"publisher":"ML Research Press","scopus_import":"1","oa_version":"Preprint","oa":1,"date_created":"2023-08-22T14:08:35Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["PMLR"],"title":"The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA","quality_controlled":"1","status":"public","month":"05","article_processing_charge":"No","date_updated":"2023-09-12T08:07:38Z","type":"conference","day":"16","department":[{"_id":"FrLo"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1905.06642"}],"abstract":[{"text":"We consider the problem of recovering a common latent source with independent\r\ncomponents from multiple views. This applies to settings in which a variable is\r\nmeasured with multiple experimental modalities, and where the goal is to\r\nsynthesize the disparate measurements into a single unified representation. We\r\nconsider the case that the observed views are a nonlinear mixing of\r\ncomponent-wise corruptions of the sources. When the views are considered\r\nseparately, this reduces to nonlinear Independent Component Analysis (ICA) for\r\nwhich it is provably impossible to undo the mixing. We present novel\r\nidentifiability proofs that this is possible when the multiple views are\r\nconsidered jointly, showing that the mixing can theoretically be undone using\r\nfunction approximators such as deep neural networks. In contrast to known\r\nidentifiability results for nonlinear ICA, we prove that independent latent\r\nsources with arbitrary mixing can be recovered as long as multiple,\r\nsufficiently different noisy views are available.","lang":"eng"}],"date_published":"2019-05-16T00:00:00Z","extern":"1","publication_status":"published"}