AAAI 2025technical0 citations
Towards Trustworthy Machine Learning Under Distribution Shifts
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).
BibTeX
@article{Wu_2025, title={Towards Trustworthy Machine Learning Under Distribution Shifts}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35125}, DOI={10.1609/aaai.v39i27.35125}, abstractNote={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).}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wu, Jun}, year={2025}, month={Apr.}, pages={28732-28732} }