Guodong Zhang

Affiliations:
  • University of Toronto, Department of Computer Science, Vector Institute, Toronto, Canada


According to our database1, Guodong Zhang authored at least 20 papers between 2018 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2022
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization.
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.
J. Mach. Learn. Res., 2021

Learning to Give Checkable Answers with Prover-Verifier Games.
CoRR, 2021

Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization.
CoRR, 2021

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise.
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.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Picking Winning Tickets Before Training by Preserving Gradient Flow.
Proceedings of the 8th International Conference on Learning Representations, 2020

On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach.
Proceedings of the 8th International Conference on Learning Representations, 2020

An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Benchmarking Model-Based Reinforcement Learning.
CoRR, 2019

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks.
CoRR, 2019

Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise.
CoRR, 2019

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks.
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.
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.
Proceedings of the 36th International Conference on Machine Learning, 2019

Three Mechanisms of Weight Decay Regularization.
Proceedings of the 7th International Conference on Learning Representations, 2019

Functional variational Bayesian Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Eigenvalue Corrected Noisy Natural Gradient.
CoRR, 2018

Noisy Natural Gradient as Variational Inference.
Proceedings of the 35th International Conference on Machine Learning, 2018

Differentiable Compositional Kernel Learning for Gaussian Processes.
Proceedings of the 35th International Conference on Machine Learning, 2018


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