{"project":[{"call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425","name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020"}],"volume":97,"intvolume":"        97","year":"2019","ec_funded":1,"citation":{"short":"N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.","ama":"Konstantinov NH, Lampert C. Robust learning from untrusted sources. In: <i>Proceedings of the 36th International Conference on Machine Learning</i>. Vol 97. ML Research Press; 2019:3488-3498.","chicago":"Konstantinov, Nikola H, and Christoph Lampert. “Robust Learning from Untrusted Sources.” In <i>Proceedings of the 36th International Conference on Machine Learning</i>, 97:3488–98. ML Research Press, 2019.","ista":"Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources. Proceedings of the 36th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 97, 3488–3498.","apa":"Konstantinov, N. H., &#38; Lampert, C. (2019). Robust learning from untrusted sources. In <i>Proceedings of the 36th International Conference on Machine Learning</i> (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.","ieee":"N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,” in <i>Proceedings of the 36th International Conference on Machine Learning</i>, Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.","mla":"Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted Sources.” <i>Proceedings of the 36th International Conference on Machine Learning</i>, vol. 97, ML Research Press, 2019, pp. 3488–98."},"conference":{"start_date":"2019-06-10","name":"ICML: International Conference on Machine Learning","end_date":"2919-06-15","location":"Long Beach, CA, USA"},"language":[{"iso":"eng"}],"_id":"6590","page":"3488-3498","author":[{"full_name":"Konstantinov, Nikola H","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","first_name":"Nikola H","last_name":"Konstantinov"},{"orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"publication":"Proceedings of the 36th International Conference on Machine Learning","external_id":{"arxiv":["1901.10310"]},"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"10799"}]},"status":"public","quality_controlled":"1","title":"Robust learning from untrusted sources","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2019-06-27T14:18:23Z","oa":1,"oa_version":"Preprint","scopus_import":"1","publisher":"ML Research Press","date_published":"2019-06-01T00:00:00Z","publication_status":"published","abstract":[{"lang":"eng","text":"Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization. "}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1901.10310"}],"department":[{"_id":"ChLa"}],"day":"01","type":"conference","date_updated":"2023-10-17T12:31:55Z","article_processing_charge":"No","month":"06"}