As machine learning continues to permeate our daily lives with the deployment of large-scale foundational models across diverse domains, we are witnessing an unprecedented era of data collection and exploration through smart devices. This abundance of data holds the potential to bring groundbreaking advancements across numerous industries and disciplines. However, effectively leveraging and safeguarding this wealth of data requires increasingly advanced mathematical techniques.
My research is centered on designing computationally efficient methods backed by theory to drive adversarial, hierarchical, and scalable machine learning models. In this talk, I will delve into my recent work on developing gradient-based optimization algorithms tailored to address game theory-related machine learning problems. Unlike traditional theories focused on convex/concave problems, my focus lies in nonconvex zero-sum games and Stackelberg games, which are essential for tackling nonconvex objective functions prevalent in neural network training. These advancements not only offer theoretical insights into stabilizing iterative numerical algorithms but also provide more generalizable solutions for downstream learning tasks. I will demonstrate the practical significance of these algorithms in addressing real-world machine learning challenges, including adversarial attacks, data hyper-cleaning, and automatic speech recognition. Furthermore, I will highlight the broader impact of the proposed learning framework on emerging problems, such as multilingual multitask learning, reinforcement learning with human feedback, and multi-agent RL.

Speaker: Dr Songtao LU
Date: 16 April 2024 (Tuesday)
Time: 9:15am – 10:15am
Poster: Click here