Wei Huang

According to our database, Wei Huang authored at least 42 papers between 2020 and 2025.

Collaborative distances:

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

2025

Provable In-Context Vector Arithmetic via Retrieving Task Concepts.

Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki

CoRR, August, 2025

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel.

Yilan Chen, Zhichao Wang, Wei Huang, Andi Han, Taiji Suzuki, Arya Mazumdar

CoRR, June, 2025

On the Role of Label Noise in the Feature Learning Process.

Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki

CoRR, May, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

On the Feature Learning in Diffusion Models.

Andi Han, Wei Huang, Yuan Cao, Difan Zou

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

NLPrompt: Noise-Label Prompt Learning for Vision-Language Models.

Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, Ye Shi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

Exact and Rich Feature Learning Dynamics of Two-Layer Linear Networks.

Wei Huang, Wuyang Chen, Zhiqiang Xu, Zhangyang Wang, Taiji Suzuki

Proceedings of the Conference on Parsimony and Learning, 2025

Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons.

Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs.

Siqing Li, Jin-Duk Park, Wei Huang, Xin Cao, Won-Yong Shin, Zhiqiang Xu

CoRR, 2024

Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method.

Bikang Pan, Wei Huang, Ye Shi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization.

Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

On the Comparison between Multi-modal and Single-modal Contrastive Learning.

Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning.

Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Provable and Efficient Dataset Distillation for Kernel Ridge Regression.

Yilan Chen, Wei Huang, Lily Weng

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs.

Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples.

Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong

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

Graph Lottery Ticket Automated.

Guibin Zhang, Kun Wang, Wei Huang, Yanwei Yue, Yang Wang, Roger Zimmermann, Aojun Zhou, Dawei Cheng, Jin Zeng, Yuxuan Liang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory.

Wei Huang, Ye Shi, Zhongyi Cai, Taiji Suzuki

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Global and Local Prompts Cooperation via Optimal Transport for Federated Learning.

Hongxia Li, Wei Huang, Jingya Wang, Ye Shi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model.

Hao Wu, Yuxuan Liang, Wei Xiong, Zhengyang Zhou, Wei Huang, Shilong Wang, Kun Wang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection.

Wei Huang, Chunrui Liu, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng

Trans. Mach. Learn. Res., 2023

Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning.

Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang

CoRR, 2023

Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model.

Hao Wu, Shilong Wang, Yuxuan Liang, Zhengyang Zhou, Wei Huang, Wei Xiong, Kun Wang

CoRR, 2023

Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective.

Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki

CoRR, 2023

Analyzing Generalization of Neural Networks through Loss Path Kernels.

Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fed-CO₂: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning.

Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization.

Wuyang Chen, Wei Huang, Zhangyang Wang

Proceedings of the International Conference on Automated Machine Learning, 2023

2022

Pruning graph neural networks by evaluating edge properties.

Li Wang, Wei Huang, Miao Zhang, Shirui Pan, Xiaojun Chang, Steven Weidong Su

Knowl. Based Syst., 2022

Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning.

Wei Huang, Chunrui Liu, Yilan Chen, Tianyu Liu, Richard Yi Da Xu

CoRR, 2022

Weighted Mutual Learning with Diversity-Driven Model Compression.

Miao Zhang, Li Wang, David Campos, Wei Huang, Chenjuan Guo, Bin Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations.

Miao Zhang, Wei Huang, Bin Yang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis.

Wuyang Chen, Wei Huang, Xinyu Gong, Boris Hanin, Zhangyang Wang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective.

Wei Huang, Yayong Li, Weitao Du, Richard Y. D. Xu, Jie Yin, Ling Chen, Miao Zhang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Auto-scaling Vision Transformers without Training.

Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Understanding deep learning through ultra-wide neural networks

Wei Huang

PhD thesis, 2021

Gaussian process latent variable model factorization for context-aware recommender systems.

Wei Huang, Richard Yi Da Xu

Pattern Recognit. Lett., 2021

Differentiable Architecture Search Without Training Nor Labels: A Pruning Perspective.

Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Wei Huang, Gholamreza Haffari

CoRR, 2021

Wide Graph Neural Networks: Aggregation Provably Leads to Exponentially Trainability Loss.

Wei Huang, Yayong Li, Weitao Du, Richard Yi Da Xu, Jie Yin, Ling Chen

CoRR, 2021

On the Equivalence between Neural Network and Support Vector Machine.

Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng

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

On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization.

Wei Huang, Weitao Du, Richard Yi Da Xu

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

2020

Implicit bias of deep linear networks in the large learning rate phase.

Wei Huang, Weitao Du, Richard Yi Da Xu, Chunrui Liu

CoRR, 2020

Mean Field Theory for Deep Dropout Networks: Digging up Gradient Backpropagation Deeply.

Wei Huang, Richard Yi Da Xu, Weitao Du, Yutian Zeng, Yunce Zhao

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020