Jiaoyang Huang

According to our database, Jiaoyang Huang authored at least 18 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 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

How Does Information Bottleneck Help Deep Learning?

Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang

Proceedings of the International Conference on Machine Learning, 2023

2022

Power Iteration for Tensor PCA.

Jiaoyang Huang, Daniel Zhengyu Huang, Qing Yang, Guang Cheng

J. Mach. Learn. Res., 2022

Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems.

Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart

CoRR, 2022

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations.

Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu

Proceedings of the Learning on Graphs Conference, 2022

Robustness Implies Generalization via Data-Dependent Generalization Bounds.

Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang

Proceedings of the International Conference on Machine Learning, 2022

2021

Long Random Matrices and Tensor Unfolding.

Gérard Ben Arous, Daniel Zhengyu Huang, Jiaoyang Huang

CoRR, 2021

Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution.

Daniel Zhengyu Huang, Jiaoyang Huang

CoRR, 2021

Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model.

Daniel Zhengyu Huang, Jiaoyang Huang

CoRR, 2021

Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization.

Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

How Shrinking Gradient Noise Helps the Performance of Neural Networks.

Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Dynamics of Deep Neural Networks and Neural Tangent Hierarchy.

Jiaoyang Huang, Horng-Tzer Yau

Proceedings of the 37th International Conference on Machine Learning, 2020

Towards Understanding the Dynamics of the First-Order Adversaries.

Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Effect of Depth and Width on Local Minima in Deep Learning.

Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling

Neural Comput., 2019

Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes.

Kenji Kawaguchi, Jiaoyang Huang

Proceedings of the 57th Annual Allerton Conference on Communication, 2019