|
"Many things are only trivial once you know them." - Herman Chernoff
About Me
I'm a PhD student in Machine Learning Group at University of Toronto, supervised by Roger Grosse.
Previously, I received my bachelor degree in Information Engineering from Zhejiang University in 2017. In 2016 summer, I was a visiting student at UCLA, where I worked with Song-Chun Zhu and Ying Nian Wu on energy-based generative models. After my visiting to UCLA, I spent half year interning at Microsoft Research Asia under the supervision of Dr. Jifeng Dai.
In 2019, I worked as a student researcher at Google Brain Toronto (with Geoffrey Hinton and Lala Li).
In early 2020, I was a visiting student at Institute of Advanced Study (IAS) for special-year program.
I'm currently working on Optimization and Generalization of Deep Learning and also Bayesian Deep Learning.
Email me if you have any questions about my paper, or if you're interested in collaborating with me.
Curriculum Vitae
My CV can be downloaded from this link: [CV].
* below indicates equal contribution
Smooth Game Optimization
-
A Unified Analysis of First-Order Methods for
Smooth Games via Integral Quadratic Constraints
Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger Grosse
ArXiv, 2020.
-
On the Suboptimality of Negative Momentum for Minimax Optimization
Guodong Zhang, Yuanhao Wang
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
-
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
Yuanhao Wang*, Guodong Zhang*, Jimmy Ba
International Conference on Learning Representations (ICLR), 2020.
Neural Network Training Dynamics
-
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise
Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
-
Which Algorithmic Choices Matter at Which Batch Sizes?
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva,
George E. Dahl, Christopher J. Shallue, Roger Grosse
Neural Information Processing Systems (NeurIPS), 2019.
-
Fast Convergence of Natural Gradient Descent
for Overparameterized Neural Networks
Guodong Zhang, James Martens, Roger Grosse
Neural Information Processing Systems (NeurIPS), 2019.
-
Three Mechanisms of Weight Decay Regularization
Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger Grosse
International Conference on Learning Representations (ICLR), 2019.
Bayesian Deep Learning
-
Functional Variational Bayesian Neural Networks
Shengyang Sun*, Guodong Zhang*, Jiaxin Shi*, Roger Grosse
International Conference on Learning Representations (ICLR), 2019.
-
Differentiable Compositional Kernel Learning for Gaussian Processes
Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse
International Conference on Machine Learning (ICML), 2018.
-
Noisy Natural Gradient as Variational Inference
Guodong Zhang*, Shengyang Sun*, David Duvenaud, Roger Grosse
International Conference on Machine Learning (ICML), 2018.
Neural Network Pruning and Computer Vision
-
Picking Winning Tickets Before Training by Preserving Gradient Flow
Chaoqi Wang, Guodong Zhang, Roger Grosse
International Conference on Learning Representations (ICLR), 2020.
-
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler, Guodong Zhang
International Conference on Machine Learning (ICML), 2019.
-
Deformable Convolutional Networks
Jifeng Dai*, Haozhi Qi*, Yuwen Xiong*, Yi Li*, Guodong Zhang*, Han Hu, Yichen Wei
IEEE International Conference on Computer Vision (ICCV), 2017.
Teaching
I am/was a TA for
- CSC 411: Machine Learning and Data Mining (2018 Fall)
- CSC 321: Introduction to Neural Networks and Machine Learning (2017 Winter)
- CSC 384: Introduction to Artificial Intelligence (2017 Fall, 2018 Summer)
Services
- Conference Reviewer: UAI, NeurIPS, ICLR, ICML
|
