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Analyzing preventing unconscious bias in machine learning. https:\/\/www.mfoq.com\/presentations\/unconscious-bias-machine-learning\/."},{"key":"e_1_3_2_1_2_1","unstructured":"Argoverse. https:\/\/www.argoverse.org\/. Argoverse. https:\/\/www.argoverse.org\/."},{"key":"e_1_3_2_1_3_1","unstructured":"CAIDA Ark Datasets. http:\/\/www.caida.org\/projects\/ark\/topo_datasets.xml. CAIDA Ark Datasets. http:\/\/www.caida.org\/projects\/ark\/topo_datasets.xml."},{"key":"e_1_3_2_1_4_1","unstructured":"CRAWDAD Datasets. https:\/\/crawdad.org\/. CRAWDAD Datasets. https:\/\/crawdad.org\/."},{"key":"e_1_3_2_1_5_1","unstructured":"If data is the new oil these companies are the new baker hughes. https:\/\/fortune.com\/2020\/02\/04\/artificial- intelligence- data- labeling- labelbox\/. 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Theipv4routed\/24topologydataset Nov. 2019."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Baxter J. A model of inductive bias learning. Journal of artificial intelligence research 12 (2000) 149--198. Baxter J. A model of inductive bias learning. Journal of artificial intelligence research 12 (2000) 149--198.","DOI":"10.1613\/jair.731"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1579114.1579187"},{"volume-title":"ACL","year":"2007","author":"Bunescu R.","key":"e_1_3_2_1_13_1"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Camacho J. P\u00e9rez-Villegas A. Garc\u00eda-Teodoro P. and Maci\u00e1-Fern\u00e1ndez G. PCA-based Multivariate Statistical Network Monitoring for Anomaly Detection. Computers & Security (2016). Camacho J. P\u00e9rez-Villegas A. Garc\u00eda-Teodoro P. and Maci\u00e1-Fern\u00e1ndez G. PCA-based Multivariate Statistical Network Monitoring for Anomaly Detection. Computers & Security (2016).","DOI":"10.1016\/j.cose.2016.02.008"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Caruana R. Multitask learning. Machine learning 28 1 (1997) 41--75. Caruana R. Multitask learning. Machine learning 28 1 (1997) 41--75.","DOI":"10.1023\/A:1007379606734"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.067"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_18_1","first-page":"2","article-title":"Privacy preserving data sharing with anonymous id assignment","volume":"8","author":"Dunning L. A.","year":"2012","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_2_1_19_1","unstructured":"El Emam K. Jonker E. and Luk Arbuckle B. M. A systematic review of re-identification attacks on health data. PloS one 6 12 (2011). El Emam K. Jonker E. and Luk Arbuckle B. M. A systematic review of re-identification attacks on health data. PloS one 6 12 (2011)."},{"key":"e_1_3_2_1_20_1","unstructured":"Feamster N. and Rexford J. Why (and how) networks should run themselves. arXiv preprint arXiv: 1710.11583 (2017). Feamster N. and Rexford J. Why (and how) networks should run themselves. arXiv preprint arXiv: 1710.11583 (2017)."},{"volume-title":"NIPS Workshop on Machine Learning Open Source Software","year":"2018","author":"Forde J.","key":"e_1_3_2_1_21_1"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3365609.3365857"},{"key":"e_1_3_2_1_24_1","unstructured":"Janai J. G\u00fcney F. Behl A. and Geiger A. Computer Vision for Autonomous Vehicles: Problems Datasets and State of the Art. arXiv e-prints (2017). Janai J. G\u00fcney F. Behl A. and Geiger A. Computer Vision for Autonomous Vehicles: Problems Datasets and State of the Art. arXiv e-prints (2017)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1080173.1080187"},{"volume-title":"ACM SIGCOMM","year":"2004","author":"Lakhina A.","key":"e_1_3_2_1_26_1"},{"key":"e_1_3_2_1_27_1","unstructured":"Lakhina A. Crovella M. and Diot C. Mining Anomalies Using Traffic Feature Distributions. ACM SIGCOMM (2005). Lakhina A. Crovella M. and Diot C. Mining Anomalies Using Traffic Feature Distributions. ACM SIGCOMM (2005)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Li X. Bian F. Zhang H. Diot C. Govindan R. Hong W. and Iannaccone G. MIND: A Distributed Multi-Dimensional Indexing System for Network Diagnosis. In IEEE INFOCOM (2006). Li X. Bian F. Zhang H. Diot C. Govindan R. Hong W. and Iannaccone G. MIND: A Distributed Multi-Dimensional Indexing System for Network Diagnosis. In IEEE INFOCOM (2006).","DOI":"10.1109\/INFOCOM.2006.205"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/NAS.2019.8834723"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1145\/1879141.1879171","volume-title":"Proceedings of the 10th ACM SIGCOMM conference on Internet measurement","author":"Luckie M.","year":"2010"},{"volume-title":"ACM SIGMETRICS","year":"2005","author":"Moore A. W.","key":"e_1_3_2_1_31_1"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00089"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Nguyen T. T. and Armitage G. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials 10 4 56--76. Nguyen T. T. and Armitage G. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials 10 4 56--76.","DOI":"10.1109\/SURV.2008.080406"},{"volume-title":"Academic Press","year":"2019","author":"Nixon M.","key":"e_1_3_2_1_34_1"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1364\/JOCN.10.00D126"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Ratner A. Bach S. H. Ehrenberg H. Fries J. Wu S. and R\u00e9 C. Snorkel: Rapid training data creation with weak supervision. VLDB Endowment (2017). Ratner A. Bach S. H. Ehrenberg H. Fries J. Wu S. and R\u00e9 C. Snorkel: Rapid training data creation with weak supervision. VLDB Endowment (2017).","DOI":"10.14778\/3157794.3157797"},{"key":"e_1_3_2_1_37_1","first-page":"3567","volume-title":"Advances in neural information processing systems","author":"Ratner A. J.","year":"2016"},{"key":"e_1_3_2_1_38_1","unstructured":"Rekatsinas T. Chu X. Ilyas I. F. and R\u00e9 C. Holoclean: Holistic data repairs with probabilistic inference. VLDB Endowment (2017). Rekatsinas T. Chu X. Ilyas I. F. and R\u00e9 C. Holoclean: Holistic data repairs with probabilistic inference. VLDB Endowment (2017)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"volume-title":"ACM IMC","year":"2017","author":"Sommers J.","key":"e_1_3_2_1_41_1"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.23919\/IFIPNetworking.2018.8696566"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916648"},{"key":"e_1_3_2_1_44_1","unstructured":"Vaidya A. Mai F. and Ning Y. Empirical analysis of multi-task learning for reducing model bias in toxic comment detection. arXiv preprint arXiv:1909.09758 (2019). Vaidya A. Mai F. and Ning Y. Empirical analysis of multi-task learning for reducing model bias in toxic comment detection. arXiv preprint arXiv:1909.09758 (2019)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/3291264.3291268"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaf5062"},{"key":"e_1_3_2_1_47_1","unstructured":"Williams N. Zander S. and Armitage G. A preliminary performance comparison of five machine learning algorithms for practical ip traffic flow classification. ACM SIGCOMM CCR (2006). Williams N. Zander S. and Armitage G. A preliminary performance comparison of five machine learning algorithms for practical ip traffic flow classification. ACM SIGCOMM CCR (2006)."},{"key":"e_1_3_2_1_48_1","unstructured":"Yuen M.-C. King I. and Leung K.-S. A survey of crowdsourcing systems. In IEEE SocialCom (2011). Yuen M.-C. King I. and Leung K.-S. A survey of crowdsourcing systems. 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