ML Anthology

Towards Trustworthy Machine Learning Under Distribution Shifts

Jun Wu
AAAI 2025 pp. 28732
doi:10.1609/AAAI.V39I27.35125 /aaai/2025/wu2025aaai-trustworthy/

Abstract

Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. It involves two key challenges: distribution shifts and trustworthiness concerns. Having these challenges in mind, my research focuses on understanding transfer learning from the perspective of knowledge transferability (e.g., IID and non-IID learning tasks) and trustworthiness (e.g., adversarial robustness, data privacy, and performance fairness).

AAAI Semantic Scholar

Cite

Text

Wu. "Towards Trustworthy Machine Learning Under Distribution Shifts." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I27.35125

Markdown

[Wu. "Towards Trustworthy Machine Learning Under Distribution Shifts." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/wu2025aaai-trustworthy/) doi:10.1609/AAAI.V39I27.35125

BibTeX

@inproceedings{wu2025aaai-trustworthy,
  title     = {{Towards Trustworthy Machine Learning Under Distribution Shifts}},
  author    = {Wu, Jun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {28732},
  doi       = {10.1609/AAAI.V39I27.35125},
  url       = {https://mlanthology.org/aaai/2025/wu2025aaai-trustworthy/}
}