Derivative-Free Optimization: Theoretical Foundations, Algorithms, and Applications
暫譯: 無導數優化：理論基礎、演算法與應用

Yang Yu is a professor at Nanjing University, specializing in artificial intelligence, machine learning, and optimization. His research focuses on derivative-free optimization, AutoML, and reinforcement learning. Prof. Yu has an extensive publication record in leading journals and conferences, including Artificial Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence, ICML, NeurIPS, IJCAI, and AAAI. He is a co-author of the book Evolutionary Learning: Advances in Theories and Algorithms (Springer, 2019). His work has introduced foundational frameworks and algorithms in classification-based optimization, notably Racos and SRacos, and contributed to the development of the optimization toolbox ZOOpt, widely utilized in academic and industrial research.

Hong Qian is an associate professor at East China Normal University, with expertise in optimization algorithms, machine learning, and computational intelligence. His research focuses on developing scalable derivative-free optimization techniques for high-dimensional problems with theoretical guarantees, and LLM for optimization. Dr. Qian has published extensively in prominent venues such as ICML, NeurIPS, AAAI, and IEEE Transactions on Evolutionary Computation and has contributed to advancements in sampling-and-classification frameworks and their applications in machine learning and optimization tasks.

Yi-Qi Hu is an AI technical expert in Huawei Co. Ltd., with expertise in machine learning, optimization algorithms, and large language model on device. His work focuses on developing machine learning systems utilizing derivative-free optimization techniques. Dr. Hu has published extensively in prominent venues such as AAAI and IJCAI and has contributed to advancements in derivative-free optimization-based AutoML systems.

楊宇是南京大學的教授，專攻人工智慧、機器學習和優化。他的研究重點在於無導數優化（derivative-free optimization）、自動機器學習（AutoML）和強化學習（reinforcement learning）。楊教授在多個領先的期刊和會議上發表了大量論文，包括《人工智慧》（Artificial Intelligence）、《IEEE模式分析與機器智慧學報》（IEEE Transactions on Pattern Analysis and Machine Intelligence）、ICML、NeurIPS、IJCAI和AAAI。他是書籍《進化學習：理論與演算法的進展》（Evolutionary Learning: Advances in Theories and Algorithms，Springer，2019）的共同作者。他的工作引入了基於分類的優化的基礎框架和演算法，特別是Racos和SRacos，並對優化工具箱ZOOpt的發展做出了貢獻，該工具箱在學術和工業研究中被廣泛使用。

錢洪是華東師範大學的副教授，專長於優化演算法、機器學習和計算智能。他的研究專注於開發可擴展的無導數優化技術，針對具有理論保證的高維問題，以及用於優化的大型語言模型（LLM）。錢博士在ICML、NeurIPS、AAAI和《IEEE進化計算學報》（IEEE Transactions on Evolutionary Computation）等重要會議上發表了大量論文，並對取樣與分類框架及其在機器學習和優化任務中的應用做出了貢獻。

胡怡琦是華為技術有限公司的人工智慧技術專家，專長於機器學習、優化演算法和設備上的大型語言模型。他的工作專注於開發利用無導數優化技術的機器學習系統。胡博士在AAAI和IJCAI等重要會議上發表了大量論文，並對基於無導數優化的自動機器學習系統的進展做出了貢獻。