Differentiable Compositional Kernel Learning for Gaussian Processes. - Shengyang Sun; Guodong Zhang 006; Chaoqi Wang; Wenyuan Zeng; Jiaman Li; Roger B. Grosse (ICML, 2018)

Noisy Natural Gradient as Variational Inference. - Guodong Zhang 006; Shengyang Sun; David Duvenaud; Roger B. Grosse (ICML, 2018)

Eigenvalue Corrected Noisy Natural Gradient. - Juhan Bae; Guodong Zhang 006; Roger B. Grosse (CoRR, 2018)

Functional variational Bayesian Neural Networks. - Shengyang Sun; Guodong Zhang 006; Jiaxin Shi; Roger B. Grosse (ICLR (Poster), 2019)

Three Mechanisms of Weight Decay Regularization. - Guodong Zhang 006; Chaoqi Wang; Bowen Xu; Roger B. Grosse (ICLR (Poster), 2019)

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. - Chaoqi Wang; Roger B. Grosse; Sanja Fidler; Guodong Zhang 006 (ICML, 2019)

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. - Guodong Zhang 006; Lala Li; Zachary Nado; James Martens; Sushant Sachdeva; George E. Dahl; Christopher J. Shallue; Roger B. Grosse (NeurIPS, 2019)

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks. - Guodong Zhang 006; James Martens; Roger B. Grosse (NeurIPS, 2019)

Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise. - Yeming Wen; Kevin Luk; Maxime Gazeau; Guodong Zhang 006; Harris Chan; Jimmy Ba (CoRR, 2019)

Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks. - Guodong Zhang 006; James Martens; Roger B. Grosse (CoRR, 2019)

Benchmarking Model-Based Reinforcement Learning. - Tingwu Wang; Xuchan Bao; Ignasi Clavera; Jerrick Hoang; Yeming Wen; Eric D. Langlois 002; Shunshi Zhang 001; Guodong Zhang 006; Pieter Abbeel; Jimmy Ba (CoRR, 2019)

An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise. - Yeming Wen; Kevin Luk; Maxime Gazeau; Guodong Zhang 006; Harris Chan; Jimmy Ba (AISTATS, 2020)

On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach. - Yuanhao Wang 001; Guodong Zhang 006; Jimmy Ba (ICLR, 2020)

Picking Winning Tickets Before Training by Preserving Gradient Flow. - Chaoqi Wang; Guodong Zhang 006; Roger B. Grosse (ICLR, 2020)

On the Suboptimality of Negative Momentum for Minimax Optimization. - Guodong Zhang 006; Yuanhao Wang 001 (AISTATS, 2021)

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. - Guodong Zhang 006; Kyle Hsu; Jianing Li; Chelsea Finn; Roger B. Grosse (NeurIPS, 2021)

Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. - Guodong Zhang 006; Yuanhao Wang 001; Laurent Lessard; Roger B. Grosse (CoRR, 2021)

Learning to Give Checkable Answers with Prover-Verifier Games. - Cem Anil; Guodong Zhang 006; Yuhuai Wu; Roger B. Grosse (CoRR, 2021)

A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints. - Guodong Zhang 006; Xuchan Bao; Laurent Lessard; Roger B. Grosse (J. Mach. Learn. Res., 2021)

Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. - Guodong Zhang 006; Yuanhao Wang 001; Laurent Lessard; Roger B. Grosse (AISTATS, 2022)

Deep Learning Dynamics: From Minimization to Games - Guodong Zhang 006 (2023)