Andi Han - RankMe

80 papers A* 17A 1B 2C 1Journal 56Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations. CoRR Dai Shi, Lequan Lin, Andi Han, Luke Thompson, José Miguel Hernández-Lobato, Zhiyong Wang, Junbin Gao
2026	A*	`conf`	On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD. AAAI Tongcheng Zhang, Zhanpeng Zhou, Mingze Wang, Andi Han, Wei Huang, Taiji Suzuki, Junchi Yan
2026	J	`jnl`	On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD. CoRR Tongcheng Zhang, Zhanpeng Zhou, Mingze Wang, Andi Han, Wei Huang, Taiji Suzuki, Junchi Yan
2026	J	`jnl`	Wiener Chaos Expansion based Neural Operator for Singular Stochastic Partial Differential Equations. CoRR Dai Shi, Luke Thompson, Andi Han, Peiyan Hu, Junbin Gao, José Miguel Hernández-Lobato
2025	J	`jnl`	A Minimalist Optimizer Design for LLM Pretraining. CoRR Athanasios Glentis, Jiaxiang Li, Andi Han, Mingyi Hong
2025	J	`jnl`	ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking. CoRR Lequan Lin, Dai Shi, Andi Han, Feng Chen, Qiuzheng Chen, Jiawen Li, Zhaoyang Li, Jiyuan Li, Zhenbang Sun, Junbin Gao
2025	J	`jnl`	ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics. CoRR Luke Thompson, Davy Guan, Dai Shi, Slade Matthews, Junbin Gao, Andi Han
2025	A*	`conf`	Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? ICML Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou
2025	J	`jnl`	Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? CoRR Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou
2025	J	`jnl`	Consistency Is Not Always Correct: Towards Understanding the Role of Exploration in Post-Training Reasoning. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Bo Xue, Qingfu Zhang, Hau-San Wong, Taiji Suzuki
2025	J	`jnl`	Design your own universe: a physics-informed agnostic method for enhancing graph neural networks. Int. J. Mach. Learn. Cybern. Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao
2025	A*	`conf`	Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning. ICLR Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao
2025	A*	`conf`	Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method. ICML Andi Han, Pierre-Louis Poirion, Akiko Takeda
2025	J	`jnl`	Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method. CoRR Andi Han, Pierre-Louis Poirion, Akiko Takeda
2025	J	`jnl`	Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel. CoRR Yilan Chen, Zhichao Wang, Wei Huang, Andi Han, Taiji Suzuki, Arya Mazumdar
2025	J	`jnl`	Generalized infinite dimensional Alpha-Procrustes based geometries. CoRR Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
2025	J	`jnl`	How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime? CoRR Wei Huang, Andi Han, Yujin Song, Yilan Chen, Denny Wu, Difan Zou, Taiji Suzuki
2025	A*	`conf`	On the Feature Learning in Diffusion Models. ICLR Andi Han, Wei Huang, Yuan Cao, Difan Zou
2025	A*	`conf`	On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. ICLR Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen
2025	A*	`conf`	On the Role of Label Noise in the Feature Learning Process. ICML Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki
2025	J	`jnl`	On the Role of Label Noise in the Feature Learning Process. CoRR Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki
2025	J	`jnl`	Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Hau-San Wong, Qingfu Zhang, Taiji Suzuki
2025	A*	`conf`	Provable In-Context Vector Arithmetic via Retrieving Task Concepts. ICML Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki
2025	J	`jnl`	Provable In-Context Vector Arithmetic via Retrieving Task Concepts. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki
2025	J	`jnl`	Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking. CoRR Athanasios Glentis, Jiaxiang Li, Qiulin Shang, Andi Han, Ioannis C. Tsaknakis, Quan Wei, Mingyi Hong
2025	J	`jnl`	Self-Contradiction as Self-Improvement: Mitigating the Generation-Understanding Gap in MLLMs. CoRR Yujin Han, Hao Chen, Andi Han, Zhiheng Wang, Xinyu Lin, Yingya Zhang, Shiwei Zhang, Difan Zou
2025	B	`conf`	SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting. IJCNN Lequan Lin, Dai Shi, Andi Han, Junbin Gao
2025	A*	`conf`	The Adaptive Complexity of Finding a Stationary Point. COLT Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama
2025	J	`jnl`	The Adaptive Complexity of Finding a Stationary Point. CoRR Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama
2025	A*	`conf`	When Graph Neural Networks Meet Dynamic Mode Decomposition. ICLR Dai Shi, Lequan Lin, Andi Han, Zhiyong Wang, Yi Guo, Junbin Gao
2024	A*	`conf`	A Framework for Bilevel Optimization on Riemannian Manifolds. NeurIPS Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Akiko Takeda
2024	J	`jnl`	A Framework for Bilevel Optimization on Riemannian Manifolds. CoRR Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda
2024	J	`jnl`	A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs. IEEE Trans. Artif. Intell. Chunya Zou, Andi Han, Lequan Lin, Ming Li, Junbin Gao
2024	J	`jnl`	Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks. CoRR Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao
2024	J	`jnl`	Differentially private Riemannian optimization. Mach. Learn. Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2024	J	`jnl`	Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning. CoRR Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao
2024	J	`jnl`	Enhancing framelet GCNs with generalized p-Laplacian regularization. Int. J. Mach. Learn. Cybern. Zhiqi Shao, Dai Shi, Andi Han, Andrey Vasnev, Yi Guo, Junbin Gao
2024	J	`jnl`	From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. Trans. Mach. Learn. Res. Andi Han, Dai Shi, Lequan Lin, Junbin Gao
2024	A*	`conf`	On the Comparison between Multi-modal and Single-modal Contrastive Learning. NeurIPS Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki
2024	J	`jnl`	On the Comparison between Multi-modal and Single-modal Contrastive Learning. CoRR Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki
2024	J	`jnl`	On the Feature Learning in Diffusion Models. CoRR Andi Han, Wei Huang, Yuan Cao, Difan Zou
2024	J	`jnl`	On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. CoRR Bingrui Li, Wei Huang, Andi Han, Zhanpeng Zhou, Taiji Suzuki, Jun Zhu, Jianfei Chen
2024	A*	`conf`	Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. NeurIPS Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong
2024	J	`jnl`	Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. CoRR Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong
2024	J	`jnl`	Riemannian block SPD coupling manifold and its application to optimal transport. Mach. Learn. Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2024	A*	`conf`	Riemannian coordinate descent algorithms on matrix manifolds. ICML Andi Han, Pratik Jawanpuria, Bamdev Mishra
2024	J	`jnl`	Riemannian coordinate descent algorithms on matrix manifolds. CoRR Andi Han, Pratik Jawanpuria, Bamdev Mishra
2024	A*	`conf`	SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining. NeurIPS Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Kumar Jawanpuria, Bamdev Mishra
2024	J	`jnl`	SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining. CoRR Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Jawanpuria, Bamdev Mishra
2024	—	`conf`	Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion. BIBM Yutong Hu, Yang Tan, Andi Han, Lirong Zheng, Liang Hong, Bingxin Zhou
2024	J	`jnl`	Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion. CoRR Yutong Hu, Yang Tan, Andi Han, Lirong Zheng, Liang Hong, Bingxin Zhou
2024	J	`jnl`	SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting. CoRR Lequan Lin, Dai Shi, Andi Han, Junbin Gao
2024	J	`jnl`	Unleash Graph Neural Networks from Heavy Tuning. CoRR Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao
2024	J	`jnl`	When Graph Neural Networks Meet Dynamic Mode Decomposition. CoRR Dai Shi, Lequan Lin, Andi Han, Zhiyong Wang, Yi Guo, Junbin Gao
2023	C	`conf`	A New Perspective On the Expressive Equivalence Between Graph Convolution and Attention Models. ACML Dai Shi, Zhiqi Shao, Andi Han, Yi Guo, Junbin Gao
2023	J	`jnl`	Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges. CoRR Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao
2023	B	`conf`	Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm. IJCNN Dai Shi, Andi Han, Yi Guo, Junbin Gao
2023	J	`jnl`	From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. CoRR Andi Han, Dai Shi, Lequan Lin, Junbin Gao
2023	J	`jnl`	Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction. Trans. Mach. Learn. Res. Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra
2023	—	`conf`	Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry. GSI (1) Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2023	J	`jnl`	Nonconvex-nonconcave min-max optimization on Riemannian manifolds. Trans. Mach. Learn. Res. Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2023	A	`conf`	Riemannian Accelerated Gradient Methods via Extrapolation. AISTATS Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2023	J	`jnl`	Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds. SIAM J. Optim. Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao
2023	J	`jnl`	Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond. CoRR Zhiqi Shao, Dai Shi, Andi Han, Yi Guo, Qibin Zhao, Junbin Gao
2022	J	`jnl`	A Simple Yet Effective SVD-GCN for Directed Graphs. CoRR Chunya Zou, Andi Han, Lequan Lin, Junbin Gao
2022	J	`jnl`	Differentially private Riemannian optimization. CoRR Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2022	J	`jnl`	Generalized Laplacian Regularized Framelet GCNs. CoRR Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev, Junbin Gao
2022	J	`jnl`	Generalized energy and gradient flow via graph framelets. CoRR Andi Han, Dai Shi, Zhiqi Shao, Junbin Gao
2022	J	`jnl`	Improved Variance Reduction Methods for Riemannian Non-Convex Optimization. IEEE Trans. Pattern Anal. Mach. Intell. Andi Han, Junbin Gao
2022	J	`jnl`	Riemannian Hamiltonian methods for min-max optimization on manifolds. CoRR Andi Han, Bamdev Mishra, Pratik Jawanpuria, Pawan Kumar, Junbin Gao
2022	J	`jnl`	Riemannian accelerated gradient methods via extrapolation. CoRR Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2022	J	`jnl`	Rieoptax: Riemannian Optimization in JAX. CoRR Saiteja Utpala, Andi Han, Pratik Jawanpuria, Bamdev Mishra
2022	—	`conf`	Robust Denoising in Graph Neural Networks. SSCI Siying Zhang, Andi Han, Junbin Gao
2021	J	`jnl`	A Discussion On the Validity of Manifold Learning. CoRR Dai Shi, Andi Han, Yi Guo, Junbin Gao
2021	A*	`conf`	On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. NeurIPS Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Junbin Gao
2021	J	`jnl`	On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. CoRR Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao
2021	A*	`conf`	Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization. IJCAI Andi Han, Junbin Gao
2020	J	`jnl`	Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient. CoRR Andi Han, Junbin Gao
2020	J	`jnl`	Riemannian stochastic recursive momentum method for non-convex optimization. CoRR Andi Han, Junbin Gao
2020	J	`jnl`	Variance reduction for Riemannian non-convex optimization with batch size adaptation. CoRR Andi Han, Junbin Gao