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2020 – today
- 2024
[j9]Andi Han
, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Differentially private Riemannian optimization. Mach. Learn. 113(3): 1133-1161 (2024)- [j8]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Riemannian block SPD coupling manifold and its application to optimal transport. Mach. Learn. 113(4): 1595-1622 (2024) - [j7]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): 1553-1573 (2024) - [j6]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): 1647-1657 (2024) - [j5]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) - [c8]Andi Han, Pratik Jawanpuria, Bamdev Mishra:
Riemannian coordinate descent algorithms on matrix manifolds. ICML 2024 - [i25]Lequan Lin, Dai Shi, Andi Han, Junbin Gao:
SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting. CoRR abs/2401.08119 (2024) - [i24]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 abs/2401.14580 (2024) - [i23]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda:
A Framework for Bilevel Optimization on Riemannian Manifolds. CoRR abs/2402.03883 (2024) - [i22]Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao:
Unleash Graph Neural Networks from Heavy Tuning. CoRR abs/2405.12521 (2024) - [i21]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 abs/2406.02214 (2024) - [i20]Andi Han, Pratik Jawanpuria, Bamdev Mishra:
Riemannian coordinate descent algorithms on matrix manifolds. CoRR abs/2406.02225 (2024) - [i19]Yutong Hu, Yang Tan, Andi Han, Lirong Zheng, Liang Hong, Bingxin Zhou:
Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion. CoRR abs/2407.07443 (2024) - [i18]Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen:
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. CoRR abs/2410.04870 (2024) - [i17]Dai Shi, Lequan Lin, Andi Han, Zhiyong Wang, Yi Guo, Junbin Gao:
When Graph Neural Networks Meet Dynamic Mode Decomposition. CoRR abs/2410.05593 (2024) - [i16]Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao:
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning. CoRR abs/2410.05697 (2024) - 2023
- [j4]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao:
Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds. SIAM J. Optim. 33(3): 1797-1827 (2023) - [j3]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Nonconvex-nonconcave min-max optimization on Riemannian manifolds. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra:
Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction. Trans. Mach. Learn. Res. 2023 (2023) - [c7]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: 1199-1214 - [c6]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Riemannian Accelerated Gradient Methods via Extrapolation. AISTATS 2023: 1554-1585 - [c5]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry. GSI (1) 2023: 405-415 - [c4]Dai Shi, Andi Han, Yi Guo, Junbin Gao:
Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm. IJCNN 2023: 1-9 - [i15]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 abs/2309.02769 (2023) - [i14]Andi Han, Dai Shi, Lequan Lin, Junbin Gao:
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. CoRR abs/2310.10121 (2023) - [i13]Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao:
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges. CoRR abs/2311.07073 (2023) - 2022
- [j1]Andi Han, Junbin Gao:
Improved Variance Reduction Methods for Riemannian Non-Convex Optimization. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7610-7623 (2022) - [c3]Siying Zhang, Andi Han, Junbin Gao:
Robust Denoising in Graph Neural Networks. SSCI 2022: 1088-1095 - [i12]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao:
Riemannian Hamiltonian methods for min-max optimization on manifolds. CoRR abs/2204.11418 (2022) - [i11]Chunya Zou, Andi Han, Lequan Lin, Junbin Gao:
A Simple Yet Effective SVD-GCN for Directed Graphs. CoRR abs/2205.09335 (2022) - [i10]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Differentially private Riemannian optimization. CoRR abs/2205.09494 (2022) - [i9]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Riemannian accelerated gradient methods via extrapolation. CoRR abs/2208.06619 (2022) - [i8]Andi Han, Dai Shi, Zhiqi Shao, Junbin Gao:
Generalized energy and gradient flow via graph framelets. CoRR abs/2210.04124 (2022) - [i7]Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra:
Rieoptax: Riemannian Optimization in JAX. CoRR abs/2210.04840 (2022) - [i6]Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev, Junbin Gao:
Generalized Laplacian Regularized Framelet GCNs. CoRR abs/2210.15092 (2022) - 2021
- [c2]Andi Han, Junbin Gao:
Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization. IJCAI 2021: 2505-2511 - [c1]Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao:
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. NeurIPS 2021: 8940-8953 - [i5]Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. CoRR abs/2106.00286 (2021) - [i4]Dai Shi, Andi Han, Yi Guo, Junbin Gao:
A Discussion On the Validity of Manifold Learning. CoRR abs/2106.01608 (2021) - 2020
- [i3]Andi Han, Junbin Gao:
Variance reduction for Riemannian non-convex optimization with batch size adaptation. CoRR abs/2007.01494 (2020) - [i2]Andi Han, Junbin Gao:
Riemannian stochastic recursive momentum method for non-convex optimization. CoRR abs/2008.04555 (2020) - [i1]Andi Han, Junbin Gao:
Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient. CoRR abs/2010.12191 (2020)
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