Jiaoyang Huang

32 papers A* 6Journal 23Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities. SIAM J. Math. Anal. José A. Carrillo, Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Dongyi Wei
2025	J	`jnl`	Convergence Analysis of Probability Flow ODE for Score-Based Generative Models. IEEE Trans. Inf. Theory Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
2025	J	`jnl`	Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models. CoRR Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
2024	J	`jnl`	Convergence Analysis of Probability Flow ODE for Score-based Generative Models. CoRR Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin
2024	J	`jnl`	Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows. CoRR Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2024	J	`jnl`	Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities. CoRR José A. Carrillo, Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Dongyi Wei
2024	A*	`conf`	High-dimensional SGD aligns with emerging outlier eigenspaces. ICLR Gérard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath
2023	J	`jnl`	Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance. CoRR Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2023	J	`jnl`	High-dimensional SGD aligns with emerging outlier eigenspaces. CoRR Gérard Ben Arous, Reza Gheissari, Jiaoyang Huang, Aukosh Jagannath
2023	A*	`conf`	How Does Information Bottleneck Help Deep Learning? ICML Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang
2023	J	`jnl`	How Does Information Bottleneck Help Deep Learning? CoRR Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang
2023	J	`jnl`	Sampling via Gradient Flows in the Space of Probability Measures. CoRR Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2022	J	`jnl`	Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems. CoRR Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
2022	—	`conf`	PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. LoG Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu
2022	J	`jnl`	PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations. CoRR Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Li-Ping Liu
2022	J	`jnl`	Power Iteration for Tensor PCA. J. Mach. Learn. Res. Jiaoyang Huang, Daniel Zhengyu Huang, Qing Yang, Guang Cheng
2022	A*	`conf`	Robustness Implies Generalization via Data-Dependent Generalization Bounds. ICML Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang
2022	J	`jnl`	Robustness Implies Generalization via Data-Dependent Generalization Bounds. CoRR Kenji Kawaguchi, Zhun Deng, Kyle Luh, Jiaoyang Huang
2021	—	`conf`	How Shrinking Gradient Noise Helps the Performance of Neural Networks. IEEE BigData Zhun Deng, Jiaoyang Huang, Kenji Kawaguchi
2021	J	`jnl`	Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model. CoRR Daniel Zhengyu Huang, Jiaoyang Huang
2021	J	`jnl`	Long Random Matrices and Tensor Unfolding. CoRR Gérard Ben Arous, Daniel Zhengyu Huang, Jiaoyang Huang
2021	A*	`conf`	Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization. NeurIPS Clement Gehring, Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling
2021	J	`jnl`	Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution. CoRR Daniel Zhengyu Huang, Jiaoyang Huang
2020	A*	`conf`	Dynamics of Deep Neural Networks and Neural Tangent Hierarchy. ICML Jiaoyang Huang, Horng-Tzer Yau
2020	A*	`conf`	Towards Understanding the Dynamics of the First-Order Adversaries. ICML Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su
2020	J	`jnl`	Towards Understanding the Dynamics of the First-Order Adversaries. CoRR Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie J. Su
2019	J	`jnl`	Dynamics of Deep Neural Networks and Neural Tangent Hierarchy. CoRR Jiaoyang Huang, Horng-Tzer Yau
2019	J	`jnl`	Effect of Depth and Width on Local Minima in Deep Learning. Neural Comput. Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling
2019	J	`jnl`	Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning. Neural Comput. Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling
2019	—	`conf`	Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes. Allerton Kenji Kawaguchi, Jiaoyang Huang
2019	J	`jnl`	Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes. CoRR Kenji Kawaguchi, Jiaoyang Huang
2018	J	`jnl`	Effect of Depth and Width on Local Minima in Deep Learning. CoRR Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling