Andi Han

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 38

Top Topics

Tikhonov Regularization

Variance Reduction

Top Venues

Trans. Mach. Learn. Res.

Publications

Chunya Zou, Andi Han, Lequan Lin, Ming Li, Junbin Gao
A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs. IEEE Trans. Artif. Intell. 5 (4) (2024)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda
A Framework for Bilevel Optimization on Riemannian Manifolds. CoRR (2024)
Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao
Unleash Graph Neural Networks from Heavy Tuning. CoRR (2024)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
Riemannian block SPD coupling manifold and its application to optimal transport. Mach. Learn. 113 (4) (2024)
Zhiqi Shao, Dai Shi, Andi Han, Andrey Vasnev, Yi Guo, Junbin Gao
Enhancing framelet GCNs with generalized p-Laplacian regularization. Int. J. Mach. Learn. Cybern. 15 (4) (2024)
Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining. CoRR (2024)
Andi Han, Pratik Jawanpuria, Bamdev Mishra
Riemannian coordinate descent algorithms on matrix manifolds. CoRR (2024)
Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao
Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks. CoRR (2024)
Lequan Lin, Dai Shi, Andi Han, Junbin Gao
SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting. CoRR (2024)
Yutong Hu, Yang Tan, Andi Han, Lirong Zheng, Liang Hong, Bingxin Zhou
Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion. CoRR (2024)
Andi Han, Dai Shi, Lequan Lin, Junbin Gao
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. Trans. Mach. Learn. Res. 2024 (2024)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
Differentially private Riemannian optimization. Mach. Learn. 113 (3) (2024)
Dai Shi, Zhiqi Shao, Andi Han, Yi Guo, Junbin Gao
A New Perspective On the Expressive Equivalence Between Graph Convolution and Attention Models. ACML (2023)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry. GSI (1) (2023)
Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra
Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction. Trans. Mach. Learn. Res. 2023 (2023)
Andi Han, Dai Shi, Lequan Lin, Junbin Gao
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. CoRR (2023)
Dai Shi, Andi Han, Yi Guo, Junbin Gao
Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm. IJCNN (2023)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao
Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds. SIAM J. Optim. 33 (3) (2023)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
Riemannian Accelerated Gradient Methods via Extrapolation. AISTATS (2023)
Zhiqi Shao, Dai Shi, Andi Han, Yi Guo, Qibin Zhao, Junbin Gao
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond. CoRR (2023)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
Nonconvex-nonconcave min-max optimization on Riemannian manifolds. Trans. Mach. Learn. Res. 2023 (2023)
Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges. CoRR (2023)
Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra
Rieoptax: Riemannian Optimization in JAX. CoRR (2022)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
Differentially private Riemannian optimization. CoRR (2022)
Siying Zhang, Andi Han, Junbin Gao
Robust Denoising in Graph Neural Networks. SSCI (2022)
Andi Han, Dai Shi, Zhiqi Shao, Junbin Gao
Generalized energy and gradient flow via graph framelets. CoRR (2022)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
Riemannian accelerated gradient methods via extrapolation. CoRR (2022)
Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev, Junbin Gao
Generalized Laplacian Regularized Framelet GCNs. CoRR (2022)
Andi Han, Junbin Gao
Improved Variance Reduction Methods for Riemannian Non-Convex Optimization. IEEE Trans. Pattern Anal. Mach. Intell. 44 (11) (2022)
Chunya Zou, Andi Han, Lequan Lin, Junbin Gao
A Simple Yet Effective SVD-GCN for Directed Graphs. CoRR (2022)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao
Riemannian Hamiltonian methods for min-max optimization on manifolds. CoRR (2022)
Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. NeurIPS (2021)
Andi Han, Junbin Gao
Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization. IJCAI (2021)
Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. CoRR (2021)
Dai Shi, Andi Han, Yi Guo, Junbin Gao
A Discussion On the Validity of Manifold Learning. CoRR (2021)
Andi Han, Junbin Gao
Riemannian stochastic recursive momentum method for non-convex optimization. CoRR (2020)
Andi Han, Junbin Gao
Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient. CoRR (2020)
Andi Han, Junbin Gao
Variance reduction for Riemannian non-convex optimization with batch size adaptation. CoRR (2020)