2026年3月27日，美国南加州大学计算机科学与数学领域滕尚华教授应邀为我校师生带来题为《Understanding and Characterizing Regularization》的学术讲座。讲座由张昭教授主持。
滕教授在报告中指出，经典的经验风险最小化（ERM）算法在一致收敛性无法刻画学习的多种场景下存在失效问题，而正则化虽在机器学习实践中应用广泛，却在更一般的设定下缺乏对其最优性的理论刻画；本研究聚焦于ERM失效的最简单情形——任意标签集的多类别学习，利用单包含图（OIGs）给出了与结构风险最小化、最大熵原理及贝叶斯推断相契合的最优学习算法，从中提取出首个精确刻画问题传导误差率的组合序列“霍尔复杂度”，并将该框架推广至弱假设情形，证明汉明图的最优定向可通过节点出度与节点依赖信用系统精确刻画最优学习器，最终借助最大熵规划构造出最优算法。本次报告为正则化的理论基础与最优学习器的设计提供了深刻洞见。
报告结束后，现场师生围绕正则化的理论边界、单包含图在实际问题中的应用前景以及弱假设情形下的算法设计等议题与滕教授展开了热烈交流。滕教授逐一回应了大家的提问，并就相关研究的前沿方向分享了独到见解。
报告人简介：Shang-Hua Teng is a USC University Professor of Computer Science and Mathematics. He is a fellow of SIAM, ACM, and Alfred P. Sloan Foundation, and has twice won the Gödel Prize, first in 2008, for developing smoothed analysis, and then in 2015, for designing the breakthrough scalable Laplacian solver. Citing him as, “one of the most original theoretical computer scientists in the world”, the Simons Foundation named him a 2014 Simons Investigator to pursue long-term curiosity-driven fundamental research. He also received the 2009 Fulkerson Prize, 2023 Science & Technology Award for Overseas Chinese from the China Computer Federation, 2025 ACM STOC Test of Time Award & 2011 ACM STOC Best Paper Award (for improving maximum-flow minimum-cut algorithms), 2022 ACM SIGecom Test of Time Award (for settling the complexity of computing a Nash equilibrium), 2021 ACM STOC Test of Time Award (for smoothed analysis), 2020 Phi Kappa Phi Faculty Recognition Award (2020) for his book Scalable Algorithms for Data and Network Analysis. In addition, he and collaborators developed the first optimal well-shaped Delaunay mesh generation algorithms for arbitrary three-dimensional domains, settled the Rousseeuw-Hubert regression-depth conjecture in robust statistics, and resolved two long-standing complexity-theoretical questions regarding the Sprague-Grundy theorem in combinatorial game theory. For his industry work with Xerox, NASA, Intel, IBM, Akamai, and Microsoft, he received fifteen patents in areas including compiler optimization, Internet technology, and social networks.