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ORCIDGuodong Zhang 0006
Person information
- affiliation: University of Toronto, Department of Computer Science, Vector Institute, Toronto, Canada
Other persons with the same name
- Guodong Zhang — disambiguation page
- Guodong Zhang 0001
![[0000-0002-8323-9126]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-8323-9126")
— South-Central University for Nationalities, Wuhan, China - Guodong Zhang 0002 — Tokushima University, Tokushima, Japan
- Guodong Zhang 0003 — Beihang University, School of Electronic and Information Engineering, Beijing, China
- Guodong Zhang 0004 — Tsinghua University, Shenzhen, China
- Guodong Zhang 0005 — Shenyang Aerospace University, Shenyang, China
Other persons with a similar name
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2020 – today
- 2022
[c13]Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse:
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. AISTATS 2022: 7659-7679- 2021
- [j1]Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse:
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints. J. Mach. Learn. Res. 22: 103:1-103:39 (2021) - [c12]Guodong Zhang, Yuanhao Wang:
On the Suboptimality of Negative Momentum for Minimax Optimization. AISTATS 2021: 2098-2106 - [c11]Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse:
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. NeurIPS 2021: 19398-19410 - [i17]Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse:
Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. CoRR abs/2102.09468 (2021) - [i16]Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse:
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. CoRR abs/2107.10211 (2021) - [i15]Cem Anil, Guodong Zhang, Yuhuai Wu, Roger B. Grosse:
Learning to Give Checkable Answers with Prover-Verifier Games. CoRR abs/2108.12099 (2021) - 2020
- [c10]Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba:
An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise. AISTATS 2020: 3621-3631 - [c9]Yuanhao Wang, Guodong Zhang, Jimmy Ba:
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach. ICLR 2020 - [c8]Chaoqi Wang, Guodong Zhang, Roger B. Grosse:
Picking Winning Tickets Before Training by Preserving Gradient Flow. ICLR 2020 - [i14]Chaoqi Wang, Guodong Zhang, Roger Baker Grosse:
Picking Winning Tickets Before Training by Preserving Gradient Flow. CoRR abs/2002.07376 (2020) - [i13]Guodong Zhang, Yuanhao Wang:
On the Suboptimality of Negative Momentum for Minimax Optimization. CoRR abs/2008.07459 (2020) - [i12]Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse:
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints. CoRR abs/2009.11359 (2020)
2010 – 2019
- 2019
- [c7]Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse:
Functional variational Bayesian Neural Networks. ICLR (Poster) 2019 - [c6]Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse:
Three Mechanisms of Weight Decay Regularization. ICLR (Poster) 2019 - [c5]Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang:
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. ICML 2019: 6566-6575 - [c4]Guodong Zhang, James Martens, Roger B. Grosse:
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. NeurIPS 2019: 8080-8091 - [c3]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. NeurIPS 2019: 8194-8205 - [i11]Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba:
Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise. CoRR abs/1902.08234 (2019) - [i10]Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse:
Functional Variational Bayesian Neural Networks. CoRR abs/1903.05779 (2019) - [i9]Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang:
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. CoRR abs/1905.05934 (2019) - [i8]Guodong Zhang, James Martens, Roger B. Grosse:
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks. CoRR abs/1905.10961 (2019) - [i7]Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric D. Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba:
Benchmarking Model-Based Reinforcement Learning. CoRR abs/1907.02057 (2019) - [i6]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. CoRR abs/1907.04164 (2019) - [i5]Yuanhao Wang, Guodong Zhang, Jimmy Ba:
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach. CoRR abs/1910.07512 (2019) - 2018
- [c2]Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse:
Differentiable Compositional Kernel Learning for Gaussian Processes. ICML 2018: 4835-4844 - [c1]Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse:
Noisy Natural Gradient as Variational Inference. ICML 2018: 5847-5856 - [i4]Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse:
Differentiable Compositional Kernel Learning for Gaussian Processes. CoRR abs/1806.04326 (2018) - [i3]Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse:
Three Mechanisms of Weight Decay Regularization. CoRR abs/1810.12281 (2018) - [i2]Juhan Bae, Guodong Zhang, Roger B. Grosse:
Eigenvalue Corrected Noisy Natural Gradient. CoRR abs/1811.12565 (2018) - 2017
- [i1]Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse:
Noisy Natural Gradient as Variational Inference. CoRR abs/1712.02390 (2017)
Coauthor Index
aka: Roger Baker Grosse
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