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.
Original language | English |
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Title of host publication | Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
Editors | Kate Larson |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 8535-8540 |
Number of pages | 6 |
ISBN (Electronic) | 9781956792041 |
DOIs | |
Publication status | Published - 3 Aug 2024 |
Event | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, Republic of Duration: 3 Aug 2024 → 9 Aug 2024 https://ijcai24.org/ (Conference website) https://ijcai24.org/whova-mobile-app/ (Conference program) https://www.ijcai.org/Proceedings/2024/ (Conference proceedings) |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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ISSN (Print) | 1045-0823 |
Conference
Conference | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 3/08/24 → 9/08/24 |
Internet address |
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Scopus Subject Areas
- Artificial Intelligence