{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:50:07Z","timestamp":1757310607938,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031264085"},{"type":"electronic","value":"9783031264092"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-26409-2_5","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T09:04:46Z","timestamp":1678957486000},"page":"68-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Understanding Difficulty-Based Sample Weighting with\u00a0a\u00a0Universal Difficulty Measure"],"prefix":"10.1007","author":[{"given":"Xiaoling","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ou","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiyao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyang","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"5_CR1","unstructured":"Zhou, X., Wu, O.: Which samples should be learned first: easy or hard? arXiv preprint arXiv:2110.05481 (2021)"},{"issue":"8","key":"5_CR2","doi-asserted-by":"publisher","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","volume":"29","author":"S-H Khan","year":"2018","unstructured":"Khan, S.-H., Hayat, M., Bennamoun, M., Sohel, F.-A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3573\u20133587 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"5_CR3","unstructured":"Kuma, M.-P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: NeurIPS, pp. 1\u20139 (2010)"},{"issue":"2","key":"5_CR4","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., et al.: Curriculum learning. In: ICML, pp. 41\u201348 (2009)","DOI":"10.1145\/1553374.1553380"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Wang, W., Feng, F., He, X., Nie, L., Chua, T.-S.: Denoising implicit feedback for recommendation. In: WSDM, pp. 373\u2013381 (2021)","DOI":"10.1145\/3437963.3441800"},{"key":"5_CR7","unstructured":"Castells, T., Weinzaepfel, P., Revaud, J.: SuperLoss: a generic loss for robust curriculum learning. In: NeurIPS, pp. 1\u201312 (2020)"},{"key":"5_CR8","unstructured":"Emanuel, B.-B., et al.: Asymmetric loss for multi-label classification. arXiv preprint arXiv:2009.14119 (2020)"},{"issue":"1","key":"5_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107585","volume":"110","author":"C Santiago","year":"2021","unstructured":"Santiago, C., Barata, C., Sasdelli, M., et al.: LOW: training deep neural networks by learning optimal sample weights. Pattern Recogn. 110(1), 107585 (2021)","journal-title":"Pattern Recogn."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, Y., Wang, X.: Gradient harmonized single-stage detector. In: AAAI, pp. 8577\u20138584 (2019)","DOI":"10.1609\/aaai.v33i01.33018577"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.-Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR, pp. 9260\u20139269 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Z., Yan, S., He, X., Sun, J.: Distribution alignment: a unified framework for long-tail visual recognition. In: CVPR, pp. 2361\u20132370 (2021)","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"5_CR13","unstructured":"Zhang, J., Zhu, J., Niu, G., Han, B., Sugiyama, M., Kankanhalli, M.: Geometry-aware instance-reweighted adversarial training. In: ICLR, pp. 1\u201329 (2021)"},{"key":"5_CR14","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-72379-8_1","volume-title":"Biomedical Engineering Systems and Technologies","author":"E Aguilar","year":"2021","unstructured":"Aguilar, E., Nagarajan, B., Khatun, R., Bola\u00f1os, M., Radeva, P.: Uncertainty modeling and deep learning applied to food image analysis. In: Ye, X., et al. (eds.) BIOSTEC 2020. CCIS, vol. 1400, pp. 3\u201316. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72379-8_1"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Wang, W.-Y.: Quantifying uncertainties in natural language processing tasks. In: AAAI, pp. 7322\u20137329 (2019)","DOI":"10.1609\/aaai.v33i01.33017322"},{"key":"5_CR16","unstructured":"Byrd, J., Lipton, Z.-C.: What is the effect of importance weighting in deep learning? In: ICML, pp. 1405\u20131419 (2019)"},{"issue":"1","key":"5_CR17","first-page":"1","volume":"19","author":"D Soudry","year":"2018","unstructured":"Soudry, D., Hoffer, E., Nacson, M.-S., Gunasekar, S., Srebro, N.: The implicit bias of gradient descent on separable data. J. Mach. Learn. Res. 19(1), 1\u201314 (2018)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR18","unstructured":"Chizat, L., Bach, F.: Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic loss. arXiv preprint arXiv:2002.04486 (2020)"},{"key":"5_CR19","unstructured":"Lyu, K., Li, J.: Gradient descent maximizes the margin of homogeneous neural networks. arXiv preprint arXiv:1906.05890 (2019)"},{"key":"5_CR20","unstructured":"Xu, D., Ye, Y., Ruan, C.: Understanding the role of importance weighting for deep learning. In: ICLR, pp. 1\u201320 (2020)"},{"key":"5_CR21","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning (2016)"},{"issue":"6","key":"5_CR22","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1162\/089976698300017232","volume":"10","author":"T Heskes","year":"1998","unstructured":"Heskes, T.: Bias\/variance decompositions for likelihood-based estimators. Neural Comput. 10(6), 1425\u20131433 (1998)","journal-title":"Neural Comput."},{"key":"5_CR23","unstructured":"Alex, K., Hinton, G.: Learning multiple layers of features from tiny images. Technical report (2009)"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR25","unstructured":"Shu, J., et al.: Meta-Weight-Net: learning an explicit mapping for sample weighting. In: NeurIPS, pp. 1\u201323 (2019)"},{"key":"5_CR26","unstructured":"Yang, Z., Yu, Y., You, C., Jacob, S., Yi, M.: Rethinking bias-variance trade-off for generalization of neural networks. In: ICML, pp. 10767\u201310777 (2020)"},{"key":"5_CR27","unstructured":"Shin, W., Ha, J.-W., Li, S., Cho, Y., et al.: Which strategies matter for noisy label classification? Insight into loss and uncertainty. arXiv preprint arXiv:2008.06218 (2020)"},{"key":"5_CR28","unstructured":"Chang, H.-S., Erik, L.-M., McCallum, A.: Active bias: training more accurate neural networks by emphasizing high variance samples. In: NeurIPS, pp. 1003\u20131013 (2017)"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Swayamdipta, S., et al.: Dataset cartography: mapping and diagnosing datasets with training dynamics. arXiv preprint arXiv:2009.10795 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.746"},{"key":"5_CR30","unstructured":"Agarwal, C., Hooker, S.: Estimating example difficulty using variance of gradients. arXiv preprint arXiv:2008.11600 (2020)"},{"issue":"12","key":"5_CR31","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1109\/TMI.2017.2708987","volume":"36","author":"J-M Wolterink","year":"2017","unstructured":"Wolterink, J.-M., Leiner, T., et al.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging 36(12), 2536\u20132545 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Lowd, D., Meek, C.: Adversarial learning. In: SIGKDD, pp. 641\u2013647 (2005)","DOI":"10.1145\/1081870.1081950"},{"key":"5_CR33","unstructured":"Elsayed, G.-F., Krishnan, D., Mobahi, H., Regan, K., Bengio, S.: Large margin deep networks for classification. In: NeurIPS, pp. 850\u2013860 (2018)"},{"issue":"2","key":"5_CR34","doi-asserted-by":"publisher","first-page":"486","DOI":"10.5812\/ijem.3505","volume":"10","author":"A Ghasemi","year":"2012","unstructured":"Ghasemi, A., Zahediasl, S.: Normality tests for statistical analysis: a guide for non-statisticians. Int. J. Endocrinol. Metab. 10(2), 486\u2013489 (2012)","journal-title":"Int. J. Endocrinol. Metab."},{"key":"5_CR35","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML, pp. 1050\u20131059 (2016)"},{"issue":"1","key":"5_CR36","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","volume":"76","author":"M Abdar","year":"2021","unstructured":"Abdar, M., et al.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Inf. Fusion 76(1), 243\u2013297 (2021)","journal-title":"Inf. Fusion"},{"key":"5_CR37","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: NeurIPS, pp. 5575\u20135585 (2017)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26409-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T09:20:43Z","timestamp":1678958443000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26409-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031264085","9783031264092"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26409-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"236","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}