Towards Trustworthy, Efficient, and Scalable Machine Learning

Authors

  • Yezi Liu University of California Irvine

DOI:

https://doi.org/10.1609/aaai.v39i28.35217

Abstract

Throughout the development of machine learning, researchers have increasingly focused on the challenges of trustworthiness, efficiency, and scalability. Our research specifically addresses these critical aspects.
AAAI-25 / IAAI-25 / EAAI-25 Proceedings Cover

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Published

2025-04-11

How to Cite

Liu, Y. (2025). Towards Trustworthy, Efficient, and Scalable Machine Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 29279–29280. https://doi.org/10.1609/aaai.v39i28.35217

Issue

Vol. 39 No. 28: IAAI-25, EAAI-25, AAAI-25 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Doctoral Consortium Track