机器学习与数据科学博士生系列论坛（第九十五期）——Inference for Stochastic Gradient Descent: Beyond Finite Variance.
报告人：杨文昊（斯坦福大学）
时间：2025-11-27 10:00-11:00
地点：腾讯会议：331-2528-5257
摘要：
Stochastic gradient descent (SGD) plays a foundational role in large-scale machine learning, yet its behavior in realistic training environments often departs from what classical finite-variance theory assumes. Empirical studies indicate that gradient noise can be highly irregular, with occasional large fluctuations and non-Gaussian patterns. Such behavior plays a critical role in shaping optimization dynamics and model calibration.
In this talk, I will discuss an approach to inference for SGD that goes beyond the traditional finite-variance paradigm. I will first describe how SGD behaves asymptotically under realistic noise conditions, emphasizing the key differences from the standard finite-variance viewpoint. I will then introduce a general methodology for uncertainty quantification and confidence-region construction for SGD-based solutions, applicable even when the underlying gradient noise lacks finite variance. This approach provides practical tools for assessing the reliability of stochastic optimization in modern learning systems.
I will conclude with a discussion of practical implications and several open directions connecting optimization behavior with inference procedures.
论坛简介：该线上论坛是由张志华教授机器学习实验室组织，每两周主办一次（除了公共假期）。论坛每次邀请一位博士生就某个前沿课题做较为系统深入的介绍，主题包括但不限于机器学习、高维统计学、运筹优化和理论计算机科学。
