Variance reduction for Riemannian non-convex optimization with batch size adaptation. - Andi Han; Junbin Gao (CoRR, 2020)

Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient. - Andi Han; Junbin Gao (CoRR, 2020)

Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization. - Andi Han; Junbin Gao (IJCAI, 2021)

On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry. - Andi Han; Bamdev Mishra; Pratik Kumar Jawanpuria; Junbin Gao (NeurIPS, 2021)

A Discussion On the Validity of Manifold Learning. - Dai Shi; Andi Han; Yi Guo 001; Junbin Gao (CoRR, 2021)

Robust Denoising in Graph Neural Networks. - Siying Zhang; Andi Han; Junbin Gao (SSCI, 2022)

A Simple Yet Effective SVD-GCN for Directed Graphs. - Chunya Zou; Andi Han; Lequan Lin; Junbin Gao (CoRR, 2022)

Generalized energy and gradient flow via graph framelets. - Andi Han; Dai Shi; Zhiqi Shao; Junbin Gao (CoRR, 2022)

Rieoptax: Riemannian Optimization in JAX. - Saiteja Utpala; Andi Han; Pratik Jawanpuria; Bamdev Mishra (CoRR, 2022)

Generalized Laplacian Regularized Framelet GCNs. - Zhiqi Shao; Andi Han; Dai Shi; Andrey Vasnev; Junbin Gao (CoRR, 2022)

Improved Variance Reduction Methods for Riemannian Non-Convex Optimization. - Andi Han; Junbin Gao (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

A New Perspective On the Expressive Equivalence Between Graph Convolution and Attention Models. - Dai Shi; Zhiqi Shao; Andi Han; Yi Guo 001; Junbin Gao (ACML, 2023)

Riemannian Accelerated Gradient Methods via Extrapolation. - Andi Han; Bamdev Mishra; Pratik Jawanpuria; Junbin Gao (AISTATS, 2023)

Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry. - Andi Han; Bamdev Mishra; Pratik Jawanpuria; Junbin Gao (GSI (1), 2023)

Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm. - Dai Shi; Andi Han; Yi Guo 001; Junbin Gao (IJCNN, 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 001; Qibin Zhao; Junbin Gao (CoRR, 2023)

Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges. - Dai Shi; Andi Han; Lequan Lin; Yi Guo 001; Junbin Gao (CoRR, 2023)

Nonconvex-nonconcave min-max optimization on Riemannian manifolds. - Andi Han; Bamdev Mishra; Pratik Jawanpuria; Junbin Gao (Trans. Mach. Learn. Res., 2023)

Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction. - Saiteja Utpala; Andi Han; Pratik Jawanpuria; Bamdev Mishra (Trans. Mach. Learn. Res., 2023)

Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds. - Andi Han; Bamdev Mishra; Pratik Jawanpuria; Pawan Kumar 001; Junbin Gao (SIAM J. Optim., 2023)

Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion. - Yutong Hu 008; Yang Tan 001; Andi Han; Lirong Zheng 005; Liang Hong; Bingxin Zhou (BIBM, 2024)

Riemannian coordinate descent algorithms on matrix manifolds. - Andi Han; Pratik Jawanpuria; Bamdev Mishra (ICML, 2024)

Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. - Dake Bu; Wei Huang 034; Andi Han; Atsushi Nitanda; Taiji Suzuki; Qingfu Zhang 001; Hau-San Wong (NeurIPS, 2024)

SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining. - Andi Han; Jiaxiang Li; Wei Huang; Mingyi Hong 001; Akiko Takeda; Pratik Kumar Jawanpuria; Bamdev Mishra (NeurIPS, 2024)

A Framework for Bilevel Optimization on Riemannian Manifolds. - Andi Han; Bamdev Mishra; Pratik Kumar Jawanpuria; Akiko Takeda (NeurIPS, 2024)

On the Comparison between Multi-modal and Single-modal Contrastive Learning. - Wei Huang 034; Andi Han; Yongqiang Chen; Yuan Cao 006; Zhiqiang Xu 003; Taiji Suzuki (NeurIPS, 2024)

SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting. - Lequan Lin; Dai Shi; Andi Han; Junbin Gao (CoRR, 2024)

Unleash Graph Neural Networks from Heavy Tuning. - Lequan Lin; Dai Shi; Andi Han; Zhiyong Wang 001; Junbin Gao (CoRR, 2024)

Differentially private Riemannian optimization. - Andi Han; Bamdev Mishra; Pratik Jawanpuria; Junbin Gao (Mach. Learn., 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)

Riemannian block SPD coupling manifold and its application to optimal transport. - Andi Han; Bamdev Mishra; Pratik Jawanpuria; Junbin Gao (Mach. Learn., 2024)

Enhancing framelet GCNs with generalized p-Laplacian regularization. - Zhiqi Shao; Dai Shi; Andi Han; Andrey Vasnev; Yi Guo 001; Junbin Gao (Int. J. Mach. Learn. Cybern., 2024)

A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs. - Chunya Zou; Andi Han; Lequan Lin; Ming Li 065; Junbin Gao (IEEE Trans. Artif. Intell., 2024)

The Adaptive Complexity of Finding a Stationary Point. - Huanjian Zhou; Andi Han; Akiko Takeda; Masashi Sugiyama (COLT, 2025)

On the Feature Learning in Diffusion Models. - Andi Han; Wei Huang 034; Yuan Cao 006; Difan Zou (ICLR, 2025)

On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent. - Bingrui Li; Wei Huang 034; Andi Han; Zhanpeng Zhou; Taiji Suzuki; Jun Zhu; Jianfei Chen (ICLR, 2025)

Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning. - Lequan Lin; Dai Shi; Andi Han; Zhiyong Wang 001; Junbin Gao (ICLR, 2025)

When Graph Neural Networks Meet Dynamic Mode Decomposition. - Dai Shi; Lequan Lin; Andi Han; Zhiyong Wang 001; Yi Guo 001; Junbin Gao (ICLR, 2025)

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? - Yujin Han; Andi Han; Wei Huang; Chaochao Lu; Difan Zou (CoRR, 2025)

Design your own universe: a physics-informed agnostic method for enhancing graph neural networks. - Dai Shi; Andi Han; Lequan Lin; Yi Guo 001; Zhiyong Wang 001; Junbin Gao (Int. J. Mach. Learn. Cybern., 2025)

Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method. - Andi Han; Pierre-Louis Poirion; Akiko Takeda (CoRR, 2025)

On the Role of Label Noise in the Feature Learning Process. - Andi Han; Wei Huang 034; Zhanpeng Zhou; Gang Niu 001; Wuyang Chen 001; Junchi Yan; Akiko Takeda; Taiji Suzuki (CoRR, 2025)

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking. - Athanasios Glentis; Jiaxiang Li; Qiulin Shang; Andi Han; Ioannis C. Tsaknakis; Quan Wei 001; Mingyi Hong 001 (CoRR, 2025)

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel. - Yilan Chen 002; Zhichao Wang; Wei Huang 034; Andi Han; Taiji Suzuki; Arya Mazumdar (CoRR, 2025)

A Minimalist Optimizer Design for LLM Pretraining. - Athanasios Glentis; Jiaxiang Li; Andi Han; Mingyi Hong 001 (CoRR, 2025)

Self-Contradiction as Self-Improvement: Mitigating the Generation-Understanding Gap in MLLMs. - Yujin Han; Hao Chen; Andi Han; Zhiheng Wang; Xinyu Lin; Yingya Zhang; Shiwei Zhang; Difan Zou (CoRR, 2025)

Provable In-Context Vector Arithmetic via Retrieving Task Concepts. - Dake Bu; Wei Huang 034; Andi Han; Atsushi Nitanda; Qingfu Zhang 001; Hau-San Wong; Taiji Suzuki (CoRR, 2025)