Hong Kong, March 18, 2024 – In a landscape where deep learning continues to revolutionize fields from autonomous driving to natural language processing, a collaborative team from the Hong Kong University of Science and Technology (HKUST), the Hong Kong University of Science and Technology (Guangzhou), City University of Hong Kong (CityU), and the University of Illinois at Urbana-Champaign (UIUC) has released a timely and comprehensive review on multi-objective optimization (MOO) in deep learning. This work addresses the limitations of traditional single-objective optimization when faced with the complexities of real-world scenarios.
The review, authored by a team of researchers including Chen Weiyu (HKUST), Zhang Xiaoyuan (CityU), and Lin Baijing (HKUST Guangzhou) as co-first authors, and supervised by Dr. Lin Xi (CityU), Assistant Professor Zhao Han (UIUC), Professor Zhang Qingfu (CityU), and Professor Guo Tianyou (HKUST) as corresponding authors, highlights the growing need for MOO in addressing multifaceted challenges in deep learning.
The traditional single-objective optimization paradigm, while successful in many applications, struggles when confronted with the need for multi-task collaboration, resource constraints, and the delicate balance between safety and fairness. This limitation is particularly relevant in the context of large language models (LLMs) and generative AI systems, where multiple objectives often need to be considered simultaneously.
Assistant Professor Zhao Han of UIUC, whose research focuses on machine learning theory and trustworthy machine learning, including algorithmic fairness, interpretability, and multi-task optimization, brings valuable expertise to the review. Her work has been recognized with a Google Research Award.
Professor Zhang Qingfu (IEEE Fellow) of CityU, a long-time researcher in multi-objective optimization, contributes his extensive knowledge. His MOEA/D method has become a classic paradigm in the field, with nearly ten thousand citations.
Professor Guo Tianyou (IEEE Fellow) of HKUST, specializing in optimization problems in machine learning, adds his expertise. He was nominated as an AI 2000 Most Influential Scholar and will serve as the Program Chair for IJCAI-2025.
This review offers a crucial perspective on the evolving landscape of deep learning, emphasizing the importance of MOO in navigating the complexities of real-world applications. As deep learning models become more sophisticated and are deployed in increasingly complex environments, the ability to effectively balance multiple objectives will be paramount. This collaborative work provides a valuable resource for researchers and practitioners seeking to address these challenges and unlock the full potential of deep learning.
Conclusion:
The collaborative review from HKUST, CityU, and UIUC underscores the critical role of multi-objective optimization in advancing deep learning. By addressing the limitations of single-objective approaches, MOO enables the development of more robust, adaptable, and ethically sound AI systems. This work paves the way for future research and development in this crucial area, ensuring that deep learning technologies can be effectively applied to solve complex real-world problems.
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