Guodong Zhang
17 papers · 2017–2026 · 7 venues · across top CS/AI venues
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Research topics
Machine Learning
(15)
Optimization & Theory Β· Machine Learning
(14)
Deep Learning
(10)
Artificial Intelligence
(5)
Core AI
(5)
Optimization & Theory Β· Deep Learning
(3)
Mathematics & Optimization
(2)
Application Areas
(2)
Architectures
(2)
Bayesian & Probabilistic
(2)
Learning Types
(2)
Optimization
(2)
Techniques
(2)
Reinforcement Learning
(1)
Core Methods
(1)
Keywords
neural network optimization
(3)
convergence rate
(3)
gaussian process
(2)
stochastic gradient descent
(2)
convergence analysis
(2)
uncertainty estimation
(2)
smooth game
(2)
bayesian neural network
(2)
neural network
(2)
large batch training
(2)
integral quadratic constraint
(2)
variational inference
(2)
natural gradient
(2)
minimax optimization
(2)
convex optimization
(1)
network pruning
(1)
reinforcement learning
(1)
global convergence
(1)
gradient descent
(1)
gaussian processes
(1)
Papers
Dejavu: Towards Experience Feedback Learning for Embodied Intelligence
CVPR 2026
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation
ICLR 2023
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
ICLR 2022
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
AISTATS 2022
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
JMLR 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
NeurIPS 2021
On the Suboptimality of Negative Momentum for Minimax Optimization
AISTATS 2021
An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise
AISTATS 2020
Picking Winning Tickets Before Training by Preserving Gradient Flow
ICLR 2020
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
ICML 2019
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks
NeurIPS 2019
FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS
ICLR 2019
Three Mechanisms of Weight Decay Regularization
ICLR 2019
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
NeurIPS 2019
Differentiable Compositional Kernel Learning for Gaussian Processes
ICML 2018
Noisy Natural Gradient as Variational Inference
ICML 2018
Deformable Convolutional Networks
ICCV 2017