{"type":"ScholarlyArticle","title":"Trustworthy Machine Learning under Imperfect Data","authors":[{"name":"Bo Han","slug":"bo-han","given":"Bo","family":"Han","affiliation":"TMLR Group , Department of Computer Science , Hong Kong Baptist University RIKEN Center for Advanced Intelligence Project"}],"abstract":"Trustworthy machine learning (TML) under imperfect data has recently brought much attention in the data-centric fields of machine learning (ML) and artificial intelligence (AI). Specifically, there are mainly three types of imperfect data along with their challenges for ML, including i) label-level imperfection: noisy labels; ii) feature-level imperfection: adversarial examples; iii) distribution-level imperfection: out-of-distribution data. Therefore, in this paper, we systematically share our insights and solutions of TML to handle three types of imperfect data. More importantly, we discuss some new challenges in TML, which also open more opportunities for future studies, such as trustworthy foundation models, trustworthy federated learning, and trustworthy causal learning.","doi":"10.24963/ijcai.2024/978","url":"https://ijcai.org/proceedings/2024/978","pdf":"https://ijcai.org/proceedings/2024/0978.pdf","markdown":"https://ijcai.org/proceedings/2024/978.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/978","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Early Career","track":"Early Career","number":978,"pages":{"first":8535,"last":8540},"keywords":["Machine Learning: ML: Robustness","Machine Learning: ML: Trustworthy machine learning","Machine Learning: ML: Weakly supervised learning","Machine Learning: ML: Adversarial machine learning"],"videos":[],"references":[{"position":1,"raw":"[Arpit et al., 2017] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. 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