Guodong Zhang

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Shandong University ∙ UNIVERSITY OF TORONTO ∙ Microsoft

A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits

This paper establishes a central limit theorem under the assumption that...
06/10/2021 ∙ by Zengjing Chen, et al. ∙ 0 ∙ share

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

Minimax optimization has recently gained a lot of attention as adversari...
02/18/2021 ∙ by Guodong Zhang, et al. ∙ 0 ∙ share

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Computational frameworks for homogenization and multiscale stability analyses of nonlinear periodic metamaterials

This paper presents a consistent computational framework for multiscale ...
10/05/2020 ∙ by Guodong Zhang, et al. ∙ 0 ∙ share

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

The theory of integral quadratic constraints (IQCs) allows the certifica...
09/23/2020 ∙ by Guodong Zhang, et al. ∙ 0 ∙ share

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On the Suboptimality of Negative Momentum for Minimax Optimization

Smooth game optimization has recently attracted great interest in machin...
08/17/2020 ∙ by Guodong Zhang, et al. ∙ 0 ∙ share

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Picking Winning Tickets Before Training by Preserving Gradient Flow

Overparameterization has been shown to benefit both the optimization and...
02/18/2020 ∙ by Chaoqi Wang, et al. ∙ 2 ∙ share

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

Many tasks in modern machine learning can be formulated as finding equil...
10/16/2019 ∙ by Yuanhao Wang, et al. ∙ 16 ∙ share

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

Increasing the batch size is a popular way to speed up neural network tr...
07/09/2019 ∙ by Guodong Zhang, et al. ∙ 3 ∙ share

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Benchmarking Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is widely seen as having the p...
07/03/2019 ∙ by Tingwu Wang, et al. ∙ 28 ∙ share

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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks

Natural gradient descent has proven effective at mitigating the effects ...
05/27/2019 ∙ by Guodong Zhang, et al. ∙ 35 ∙ share

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EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Reducing the test time resource requirements of a neural network while p...
05/15/2019 ∙ by Chaoqi Wang, et al. ∙ 10 ∙ share

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Computational Design of Finite Strain Auxetic Metamaterials via Topology Optimization and Nonlinear Homogenization

A novel computational framework for designing metamaterials with negativ...
04/25/2019 ∙ by Guodong Zhang, et al. ∙ 0 ∙ share

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Functional Variational Bayesian Neural Networks

Variational Bayesian neural networks (BNNs) perform variational inferenc...
03/14/2019 ∙ by Shengyang Sun, et al. ∙ 34 ∙ share

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

The choice of batch-size in a stochastic optimization algorithm plays a ...
02/21/2019 ∙ by Yeming Wen, et al. ∙ 0 ∙ share

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Eigenvalue Corrected Noisy Natural Gradient

Variational Bayesian neural networks combine the flexibility of deep lea...
11/30/2018 ∙ by Juhan Bae, et al. ∙ 5 ∙ share

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Three Mechanisms of Weight Decay Regularization

Weight decay is one of the standard tricks in the neural network toolbox...
10/29/2018 ∙ by Guodong Zhang, et al. ∙ 2 ∙ share

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Differentiable Compositional Kernel Learning for Gaussian Processes

The generalization properties of Gaussian processes depend heavily on th...
06/12/2018 ∙ by Shengyang Sun, et al. ∙ 2 ∙ share

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Noisy Natural Gradient as Variational Inference

Combining the flexibility of deep learning with Bayesian uncertainty est...
12/06/2017 ∙ by Guodong Zhang, et al. ∙ 0 ∙ share

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Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geo...
03/17/2017 ∙ by Jifeng Dai, et al. ∙ 0 ∙ share

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Guodong Zhang

Featured Co-authors

Pieter Abbeel

Yi Li

Sanja Fidler

David Duvenaud

Roger Grosse

Han Hu

Jimmy Ba

Jifeng Dai

Yichen Wei

Wenyuan Zeng

Yuwen Xiong

A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits

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

Computational frameworks for homogenization and multiscale stability analyses of nonlinear periodic metamaterials

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

On the Suboptimality of Negative Momentum for Minimax Optimization

Picking Winning Tickets Before Training by Preserving Gradient Flow

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

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

Benchmarking Model-Based Reinforcement Learning

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Computational Design of Finite Strain Auxetic Metamaterials via Topology Optimization and Nonlinear Homogenization

Functional Variational Bayesian Neural Networks

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

Eigenvalue Corrected Noisy Natural Gradient

Three Mechanisms of Weight Decay Regularization

Differentiable Compositional Kernel Learning for Gaussian Processes

Noisy Natural Gradient as Variational Inference

Deformable Convolutional Networks