Andi Han

According to our database, Andi Han authored at least 35 papers between 2020 and 2024.

Collaborative distances:

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

2024

A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs.

Chunya Zou, Andi Han, Lequan Lin, Ming Li, Junbin Gao

IEEE Trans. Artif. Intell., April, 2024

Enhancing framelet GCNs with generalized p-Laplacian regularization.

Zhiqi Shao, Dai Shi, Andi Han, Andrey Vasnev, Yi Guo, Junbin Gao

Int. J. Mach. Learn. Cybern., April, 2024

Riemannian block SPD coupling manifold and its application to optimal transport.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

Mach. Learn., April, 2024

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond.

Andi Han, Dai Shi, Lequan Lin, Junbin Gao

Trans. Mach. Learn. Res., 2024

Differentially private Riemannian optimization.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

Mach. Learn., 2024

Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning.

Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao

CoRR, 2024

When Graph Neural Networks Meet Dynamic Mode Decomposition.

Dai Shi, Lequan Lin, Andi Han, Zhiyong Wang, Yi Guo, Junbin Gao

CoRR, 2024

On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent.

Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen

CoRR, 2024

Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion.

Yutong Hu, Yang Tan, Andi Han, Lirong Zheng, Liang Hong, Bingxin Zhou

CoRR, 2024

SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining.

Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra

CoRR, 2024

Unleash Graph Neural Networks from Heavy Tuning.

Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao

CoRR, 2024

A Framework for Bilevel Optimization on Riemannian Manifolds.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda

CoRR, 2024

Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks.

Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao

CoRR, 2024

SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting.

Lequan Lin, Dai Shi, Andi Han, Junbin Gao

CoRR, 2024

Riemannian coordinate descent algorithms on matrix manifolds.

Andi Han, Pratik Jawanpuria, Bamdev Mishra

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao

SIAM J. Optim., September, 2023

Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction.

Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra

Trans. Mach. Learn. Res., 2023

Nonconvex-nonconcave min-max optimization on Riemannian manifolds.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

Trans. Mach. Learn. Res., 2023

Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges.

Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao

CoRR, 2023

Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond.

Zhiqi Shao, Dai Shi, Andi Han, Yi Guo, Qibin Zhao, Junbin Gao

CoRR, 2023

Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm.

Dai Shi, Andi Han, Yi Guo, Junbin Gao

Proceedings of the International Joint Conference on Neural Networks, 2023

Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

Proceedings of the Geometric Science of Information - 6th International Conference, 2023

Riemannian Accelerated Gradient Methods via Extrapolation.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

A New Perspective On the Expressive Equivalence Between Graph Convolution and Attention Models.

Dai Shi, Zhiqi Shao, Andi Han, Yi Guo, Junbin Gao

Proceedings of the Asian Conference on Machine Learning, 2023

2022

Improved Variance Reduction Methods for Riemannian Non-Convex Optimization.

Andi Han, Junbin Gao

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Generalized Laplacian Regularized Framelet GCNs.

Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev, Junbin Gao

CoRR, 2022

Rieoptax: Riemannian Optimization in JAX.

Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra

CoRR, 2022

Generalized energy and gradient flow via graph framelets.

Andi Han, Dai Shi, Zhiqi Shao, Junbin Gao

CoRR, 2022

A Simple Yet Effective SVD-GCN for Directed Graphs.

Chunya Zou, Andi Han, Lequan Lin, Junbin Gao

CoRR, 2022

Robust Denoising in Graph Neural Networks.

Siying Zhang, Andi Han, Junbin Gao

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

2021

A Discussion On the Validity of Manifold Learning.

Dai Shi, Andi Han, Yi Guo, Junbin Gao

CoRR, 2021

On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry.

Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao

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

Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization.

Andi Han, Junbin Gao

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient.

Andi Han, Junbin Gao

CoRR, 2020

Variance reduction for Riemannian non-convex optimization with batch size adaptation.

Andi Han, Junbin Gao

CoRR, 2020