Mean Field Theory for Deep Dropout Networks: Digging up Gradient Backpropagation Deeply. - Wei Huang 034; Richard Yi Da Xu; Weitao Du; Yutian Zeng; Yunce Zhao (ECAI, 2020)

Implicit bias of deep linear networks in the large learning rate phase. - Wei Huang 034; Weitao Du; Richard Yi Da Xu; Chunrui Liu (CoRR, 2020)

On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization. - Wei Huang 034; Weitao Du; Richard Yi Da Xu (IJCAI, 2021)

On the Equivalence between Neural Network and Support Vector Machine. - Yilan Chen 002; Wei Huang 034; Lam M. Nguyen; Tsui-Wei Weng (NeurIPS, 2021)

Wide Graph Neural Networks: Aggregation Provably Leads to Exponentially Trainability Loss. - Wei Huang 034; Yayong Li; Weitao Du; Richard Yi Da Xu; Jie Yin 001; Ling Chen 006 (CoRR, 2021)

Differentiable Architecture Search Without Training Nor Labels: A Pruning Perspective. - Miao Zhang; Steven W. Su; Shirui Pan; Xiaojun Chang; Wei Huang 034; Gholamreza Haffari (CoRR, 2021)

Gaussian process latent variable model factorization for context-aware recommender systems. - Wei Huang 034; Richard Yi Da Xu (Pattern Recognit. Lett., 2021)

Understanding deep learning through ultra-wide neural networks - Wei Huang 034 (2021)

Auto-scaling Vision Transformers without Training. - Wuyang Chen 001; Wei Huang 034; Xianzhi Du; Xiaodan Song; Zhangyang Wang; Denny Zhou (ICLR, 2022)

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective. - Wei Huang 034; Yayong Li; Weitao Du; Richard Y. D. Xu; Jie Yin 001; Ling Chen 006; Miao Zhang (ICLR, 2022)

Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis. - Wuyang Chen 001; Wei Huang 034; Xinyu Gong; Boris Hanin; Zhangyang Wang (NeurIPS, 2022)

Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations. - Miao Zhang; Wei Huang 034; Bin Yang 002 (NeurIPS, 2022)

Weighted Mutual Learning with Diversity-Driven Model Compression. - Miao Zhang; Li Wang 148; David Campos 002; Wei Huang 034; Chenjuan Guo; Bin Yang 002 (NeurIPS, 2022)

Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning. - Wei Huang 034; Chunrui Liu; Yilan Chen 002; Tianyu Liu; Richard Yi Da Xu (CoRR, 2022)

Pruning graph neural networks by evaluating edge properties. - Li Wang 148; Wei Huang 034; Miao Zhang; Shirui Pan; Xiaojun Chang; Steven Weidong Su (Knowl. Based Syst., 2022)

"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization. - Wuyang Chen 001; Wei Huang 034; Zhangyang Wang (AutoML, 2023)

Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. - Zhongyi Cai; Ye Shi 001; Wei Huang 034; Jingya Wang (NeurIPS, 2023)

Analyzing Generalization of Neural Networks through Loss Path Kernels. - Yilan Chen 002; Wei Huang 034; Hao Wang; Charlotte Loh; Akash Srivastava; Lam M. Nguyen; Lily Weng (NeurIPS, 2023)

Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective. - Wei Huang 034; Yuan Cao 006; Haonan Wang; Xin Cao 001; Taiji Suzuki (CoRR, 2023)

Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. - Hao Wu; Shilong Wang 002; Yuxuan Liang; Zhengyang Zhou; Wei Huang 034; Wei Xiong; Kun Wang 042 (CoRR, 2023)

Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. - Zhongyi Cai; Ye Shi 001; Wei Huang 034; Jingya Wang (CoRR, 2023)

Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection. - Wei Huang 034; Chunrui Liu; Yilan Chen 002; Richard Yi Da Xu; Miao Zhang; Tsui-Wei Weng (Trans. Mach. Learn. Res., 2023)

Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. - Hao Wu; Yuxuan Liang; Wei Xiong; Zhengyang Zhou; Wei Huang 034; Shilong Wang 002; Kun Wang 056 (AAAI, 2024)

Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. - Hongxia Li; Wei Huang 034; Jingya Wang; Ye Shi 001 (CVPR, 2024)

Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. - Wei Huang 034; Ye Shi 001; Zhongyi Cai; Taiji Suzuki (ICLR, 2024)

Graph Lottery Ticket Automated. - Guibin Zhang; Kun Wang 056; Wei Huang 034; Yanwei Yue; Yang Wang 015; Roger Zimmermann; Aojun Zhou; Dawei Cheng; Jin Zeng; Yuxuan Liang (ICLR, 2024)

Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples. - Dake Bu; Wei Huang 034; Taiji Suzuki; Ji Cheng 001; Qingfu Zhang 001; Zhiqiang Xu 003; Hau-San Wong (ICML, 2024)

The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs. - Kun Wang 056; Guibin Zhang; Xinnan Zhang; Junfeng Fang; Xun Wu; Guohao Li 001; Shirui Pan; Wei Huang 034; Yuxuan Liang (KDD, 2024)

Provable and Efficient Dataset Distillation for Kernel Ridge Regression. - Yilan Chen 002; Wei Huang 034; Lily Weng (NeurIPS, 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)

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)

Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. - Jiarui Jiang; Wei Huang 034; Miao Zhang; Taiji Suzuki; Liqiang Nie (NeurIPS, 2024)

Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. - Bikang Pan; Wei Huang 034; Ye Shi 001 (NeurIPS, 2024)

LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs. - Siqing Li; Jin-Duk Park; Wei Huang 034; Xin Cao 001; Won-Yong Shin; Zhiqiang Xu 003 (CoRR, 2024)

Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons. - Wei Huang 034; Yuan Cao 006; Haonan Wang; Xin Cao 001; Taiji Suzuki (AISTATS, 2025)

Exact and Rich Feature Learning Dynamics of Two-Layer Linear Networks. - Wei Huang 034; Wuyang Chen 001; Zhiqiang Xu 003; Zhangyang Wang; Taiji Suzuki (CPAL, 2025)

NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. - Bikang Pan; Qun Li; Xiaoying Tang 002; Wei Huang 034; Zhen Fang; Feng Liu; Jingya Wang; Jingyi Yu; Ye Shi 001 (CVPR, 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)

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)

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)

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)