Guodong Zhang

According to our database, Guodong Zhang authored at least 20 papers between 2018 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization.

Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints.

Guodong Zhang, Xuchan Bao, Laurent Lessard, Roger B. Grosse

J. Mach. Learn. Res., 2021

Learning to Give Checkable Answers with Prover-Verifier Games.

Cem Anil, Guodong Zhang, Yuhuai Wu, Roger B. Grosse

CoRR, 2021

Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization.

Guodong Zhang, Yuanhao Wang, Laurent Lessard, Roger B. Grosse

CoRR, 2021

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise.

Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Suboptimality of Negative Momentum for Minimax Optimization.

Guodong Zhang, Yuanhao Wang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Picking Winning Tickets Before Training by Preserving Gradient Flow.

Chaoqi Wang, Guodong Zhang, Roger B. Grosse

Proceedings of the 8th International Conference on Learning Representations, 2020

On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach.

Yuanhao Wang, Guodong Zhang, Jimmy Ba

Proceedings of the 8th International Conference on Learning Representations, 2020

An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise.

Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Benchmarking Model-Based Reinforcement Learning.

Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric D. Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba

CoRR, 2019

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks.

Guodong Zhang, James Martens, Roger B. Grosse

CoRR, 2019

Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise.

Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba

CoRR, 2019

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks.

Guodong Zhang, James Martens, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model.

Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis.

Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang

Proceedings of the 36th International Conference on Machine Learning, 2019

Three Mechanisms of Weight Decay Regularization.

Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger B. Grosse

Proceedings of the 7th International Conference on Learning Representations, 2019

Functional variational Bayesian Neural Networks.

Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger B. Grosse

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Eigenvalue Corrected Noisy Natural Gradient.

Juhan Bae, Guodong Zhang, Roger B. Grosse

CoRR, 2018

Noisy Natural Gradient as Variational Inference.

Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse

Proceedings of the 35th International Conference on Machine Learning, 2018

Differentiable Compositional Kernel Learning for Gaussian Processes.

Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger B. Grosse

Proceedings of the 35th International Conference on Machine Learning, 2018