Stochastic approximation (SA) is the foundation of many online decision-making algorithms under uncertainty. In recent years, it has garnered renewed interest in the dynamic environment setting where streaming data is not independent and identically distributed (i.i.d.), but rather is adaptive and/or decision-dependent. This resurgence of interest stems from its widespread application in contemporary domains, such as reinforcement learning, performative prediction, and fine-tuning of large language models (LLMs). This presentation focuses on SA algorithms applied to the performative prediction problem(s), constituting the stochastic optimization problems with decision-dependent distributions. We will commence by motivating the problem through an illustrative example of strategic classification and demonstrate that a natural implementation of “stochastic gradient” with “greedy deployment” yields an SA scheme that deviates from standard stochastic gradient. Subsequently, we will present recent results on the convergence of such an algorithm under both convex and non-convex settings, as well as in stateful and non-stateful agent environments. Finally, we will illustrate several intriguing extensions to encompass multi-agent learning, non-cooperative network games, and fine-tuning of LLMs.

Hoi-To Wai received his PhD degree from Arizona State University (ASU) in Electrical Engineering in Fall 2017, B. Eng. (with First Class Honor) and M. Phil. degrees in Electronic Engineering from The Chinese University of Hong Kong (CUHK) in 2010 and 2012, respectively. He is an Associate Professor in the Department of Systems Engineering & Engineering Management at CUHK. He has held research positions at ASU, UC Davis, Telecom ParisTech, Ecole Polytechnique, MIT. He is also an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks, IEEE Transactions on Signal Processing, Elsevier’s Signal Processing. Hoi-To’s research interests are in the broad area of signal processing, machine learning and stochastic optimization. His dissertation has received the 2017’s Dean’s Dissertation Award from the Ira A. Fulton Schools of Engineering of ASU and he is a recipient of Best Student Paper Awards at ICASSP 2018, SAM 2024 (as a co-author), ICASSP 2025 (as a co-author).