Distinguished Lecture Series - Talk by Niao He (ETH Zurich)
We are pleased to announce our upcoming Distinguished Lecture Series talk by Niao He (ETH Zurich)! The talk will take place in person on May 2nd, in room UN32.101. Professor He will also be available for meetings on May 2nd. If you are interested in scheduling a meeting, please email .
Niao He is an Assistant Professor in the Department of Computer Science at ETH Zurich, where she leads the Optimization and Decision Intelligence (ODI) Group. She is also an ELLIS Scholar and a core faculty member of ETH AI Center, ETH-Max Planck Center of Learning Systems, and ETH Foundations of Data Science. Previously, she was an assistant professor at the University of Illinois at Urbana-Champaign from 2016 to 2020. Before that, she received her Ph.D. degree in Operations Research from Georgia Institute of Technology in 2015. Her research interests are in large-scale optimization, machine learning, and reinforcement learning. She is a recipient of AISTATS Best Paper Award, NSF CISE Research Initiation Initiative (CRII) Award, NCSA Faculty Fellowship, and Beckman CAS Fellowship. She regularly serves as an area chair for NeurIPS, ICLR, ICML and other machine learning conferences.
Title: The Puzzle of Adaptive Gradient Methods for Machine Learning
The Puzzle of Adaptive Gradient Methods for Machine Learning
A central optimization challenge in machine learning is parameter-tuning. Adaptive gradient methods, such as AdaGrad and Adam, are ubiquitously used for training machine learning models in practice, owing to their ability to adjust the stepsizes without granular knowledge of the loss functions. While these methods have shown remarkable empirical success in training deep neural networks for supervised learning tasks, they often struggle in more challenging scenarios involving adversarial learning, such as adversarial training and generative adversarial networks. In this talk, we will explore some of the most pressing questions regarding adaptive gradient methods: What are the provable benefits of adaptive methods? How can we improve their robustness and effectiveness for adversarial learning?
Date: May 2, 2023
Place: Universitätstraße 32.101, Campus Vaihingen of the University of Stuttgart.
Looking forward to seeing you all there! No registration necessary.