Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes. - Kenji Kawaguchi; Jiaoyang Huang (Allerton, 2019)

Effect of Depth and Width on Local Minima in Deep Learning. - Kenji Kawaguchi; Jiaoyang Huang; Leslie Pack Kaelbling (Neural Comput., 2019)

Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning. - Kenji Kawaguchi; Jiaoyang Huang; Leslie Pack Kaelbling (Neural Comput., 2019)

Towards Understanding the Dynamics of the First-Order Adversaries. - Zhun Deng; Hangfeng He 001; Jiaoyang Huang; Weijie J. Su (ICML, 2020)

Dynamics of Deep Neural Networks and Neural Tangent Hierarchy. - Jiaoyang Huang; Horng-Tzer Yau (ICML, 2020)

How Shrinking Gradient Noise Helps the Performance of Neural Networks. - Zhun Deng; Jiaoyang Huang; Kenji Kawaguchi (IEEE BigData, 2021)

Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. - Clement Gehring; Kenji Kawaguchi; Jiaoyang Huang; Leslie Pack Kaelbling (NeurIPS, 2021)

Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model. - Daniel Zhengyu Huang; Jiaoyang Huang (CoRR, 2021)

Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution. - Daniel Zhengyu Huang; Jiaoyang Huang (CoRR, 2021)

Long Random Matrices and Tensor Unfolding. - Gérard Ben Arous; Daniel Zhengyu Huang; Jiaoyang Huang (CoRR, 2021)

Robustness Implies Generalization via Data-Dependent Generalization Bounds. - Kenji Kawaguchi; Zhun Deng; Kyle Luh; Jiaoyang Huang (ICML, 2022)

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. - Han Gao 005; Xu Han 012; Jiaoyang Huang; Jian-Xun Wang 001; Liping Liu 001 (LoG, 2022)

Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems. - Daniel Zhengyu Huang; Jiaoyang Huang; Sebastian Reich; Andrew M. Stuart (CoRR, 2022)

Power Iteration for Tensor PCA. - Jiaoyang Huang; Daniel Zhengyu Huang; Qing Yang; Guang Cheng (J. Mach. Learn. Res., 2022)

How Does Information Bottleneck Help Deep Learning? - Kenji Kawaguchi; Zhun Deng; Xu Ji; Jiaoyang Huang (ICML, 2023)

Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance. - Yifan Chen; Daniel Zhengyu Huang; Jiaoyang Huang; Sebastian Reich; Andrew M. Stuart (CoRR, 2023)

Sampling via Gradient Flows in the Space of Probability Measures. - Yifan Chen; Daniel Zhengyu Huang; Jiaoyang Huang; Sebastian Reich; Andrew M. Stuart (CoRR, 2023)

High-dimensional SGD aligns with emerging outlier eigenspaces. - Gérard Ben Arous; Reza Gheissari; Jiaoyang Huang; Aukosh Jagannath (ICLR, 2024)

Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows. - Yifan Chen; Daniel Zhengyu Huang; Jiaoyang Huang; Sebastian Reich; Andrew M. Stuart (CoRR, 2024)

Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities. - José A. Carrillo 001; Yifan Chen; Daniel Zhengyu Huang; Jiaoyang Huang; Dongyi Wei (CoRR, 2024)

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models. - Daniel Zhengyu Huang; Jiaoyang Huang; Zhengjiang Lin (CoRR, 2025)

Convergence Analysis of Probability Flow ODE for Score-Based Generative Models. - Daniel Zhengyu Huang; Jiaoyang Huang; Zhengjiang Lin (IEEE Trans. Inf. Theory, 2025)