Wei Huang - RankMe

80 papers A* 32A 2Journal 43Unranked 2

Year	Rank	Type	Title / Venue / Authors
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
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	—	`conf`	Exact and Rich Feature Learning Dynamics of Two-Layer Linear Networks. CPAL Wei Huang, Wuyang Chen, Zhiqiang Xu, Zhangyang Wang, Taiji Suzuki
2025	A*	`conf`	GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs. ICML Yue Wang, Qizhou Wang, Feng Liu, Wei Huang, Yali Du, Xiaojiang Du, Bo Han
2025	J	`jnl`	GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models. CoRR Yue Wang, Qizhou Wang, Feng Liu, Wei Huang, Yali Du, Xiaojiang Du, Bo Han
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`	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	J	`jnl`	Mamba Can Learn Low-Dimensional Targets In-Context via Test-Time Feature Learning. CoRR Junsoo Oh, Wei Huang, Taiji Suzuki
2025	A*	`conf`	NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. CVPR Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, Ye Shi
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`	On the convergence analysis of over-parameterized variational autoencoders: a neural tangent kernel perspective. Mach. Learn. Li Wang, Wei Huang
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	A	`conf`	Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons. AISTATS Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki
2025	J	`jnl`	Scaling Diffusion Transformers Efficiently via μP. CoRR Chenyu Zheng, Xinyu Zhang, Rongzhen Wang, Wei Huang, Zhi Tian, Weilin Huang, Jun Zhu, Chongxuan Li
2025	A*	`conf`	Test-Time Graph Neural Dataset Search With Generative Projection. ICML Xin Zheng, Wei Huang, Chuan Zhou, Ming Li, Shirui Pan
2025	J	`jnl`	Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression. CoRR Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie
2025	A*	`conf`	Understanding the Forgetting of (Replay-based) Continual Learning via Feature Learning: Angle Matters. ICML Hongyi Wang, Shiyuan Ren, Wei Huang, Miao Zhang, Xiang Deng, Yixin Bao, Liqiang Nie
2024	A*	`conf`	Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. AAAI Hao Wu, Yuxuan Liang, Wei Xiong, Zhengyang Zhou, Wei Huang, Shilong Wang, Kun Wang
2024	A*	`conf`	Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. NeurIPS Bikang Pan, Wei Huang, Ye Shi
2024	J	`jnl`	Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. CoRR Bikang Pan, Wei Huang, Ye Shi
2024	A*	`conf`	Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. CVPR Hongxia Li, Wei Huang, Jingya Wang, Ye Shi
2024	J	`jnl`	Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. CoRR Hongxia Li, Wei Huang, Jingya Wang, Ye Shi
2024	A*	`conf`	Graph Lottery Ticket Automated. ICLR Guibin Zhang, Kun Wang, Wei Huang, Yanwei Yue, Yang Wang, Roger Zimmermann, Aojun Zhou, Dawei Cheng, Jin Zeng, Yuxuan Liang
2024	J	`jnl`	LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs. CoRR Siqing Li, Jin-Duk Park, Wei Huang, Xin Cao, Won-Yong Shin, Zhiqiang Xu
2024	J	`jnl`	NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. CoRR Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, Ye Shi
2024	A*	`conf`	On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability. NeurIPS Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li
2024	J	`jnl`	On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability. CoRR Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li
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`	Provable and Efficient Dataset Distillation for Kernel Ridge Regression. NeurIPS Yilan Chen, Wei Huang, Lily Weng
2024	A*	`conf`	Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples. ICML Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong
2024	J	`jnl`	Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples. CoRR Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong
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	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	A*	`conf`	The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs. KDD Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang
2024	J	`jnl`	The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs. CoRR Kun Wang, Guibin Zhang, Xinnan Zhang, Junfeng Fang, Xun Wu, Guohao Li, Shirui Pan, Wei Huang, Yuxuan Liang
2024	A*	`conf`	Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. ICLR Wei Huang, Ye Shi, Zhongyi Cai, Taiji Suzuki
2024	A*	`conf`	Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. NeurIPS Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie
2024	J	`jnl`	Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. CoRR Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie
2023	—	`conf`	"No Free Lunch" in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization. AutoML Wuyang Chen, Wei Huang, Zhangyang Wang
2023	J	`jnl`	Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection. Trans. Mach. Learn. Res. Wei Huang, Chunrui Liu, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng
2023	A*	`conf`	Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng
2023	J	`jnl`	Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. CoRR Hao Wu, Shilong Wang, Yuxuan Liang, Zhengyang Zhou, Wei Huang, Wei Xiong, Kun Wang
2023	A*	`conf`	Fed-CO NeurIPS Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang
2023	J	`jnl`	Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. CoRR Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang
2023	J	`jnl`	Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective. CoRR Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki
2023	J	`jnl`	Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng
2023	A*	`conf`	Understanding and Improving Feature Learning for Out-of-Distribution Generalization. NeurIPS Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng
2022	A*	`conf`	Auto-scaling Vision Transformers without Training. ICLR Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou
2022	J	`jnl`	Auto-scaling Vision Transformers without Training. CoRR Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou
2022	A*	`conf`	Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis. NeurIPS Wuyang Chen, Wei Huang, Xinyu Gong, Boris Hanin, Zhangyang Wang
2022	J	`jnl`	Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis. CoRR Wuyang Chen, Wei Huang, Xinyu Gong, Boris Hanin, Zhangyang Wang
2022	J	`jnl`	Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning. CoRR Wei Huang, Chunrui Liu, Yilan Chen, Tianyu Liu, Richard Yi Da Xu
2022	A*	`conf`	Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations. NeurIPS Miao Zhang, Wei Huang, Bin Yang
2022	J	`jnl`	Pruning graph neural networks by evaluating edge properties. Knowl. Based Syst. Li Wang, Wei Huang, Miao Zhang, Shirui Pan, Xiaojun Chang, Steven Weidong Su
2022	A*	`conf`	Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective. ICLR Wei Huang, Yayong Li, Weitao Du, Richard Y. D. Xu, Jie Yin, Ling Chen, Miao Zhang
2022	A*	`conf`	Weighted Mutual Learning with Diversity-Driven Model Compression. NeurIPS Miao Zhang, Li Wang, David Campos, Wei Huang, Chenjuan Guo, Bin Yang
2021	J	`jnl`	Differentiable Architecture Search Without Training Nor Labels: A Pruning Perspective. CoRR Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, Wei Huang, Gholamreza Haffari
2021	J	`jnl`	Gaussian process latent variable model factorization for context-aware recommender systems. Pattern Recognit. Lett. Wei Huang, Richard Yi Da Xu
2021	A*	`conf`	On the Equivalence between Neural Network and Support Vector Machine. NeurIPS Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng
2021	J	`jnl`	On the Equivalence between Neural Network and Support Vector Machine. CoRR Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng
2021	A*	`conf`	On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization. IJCAI Wei Huang, Weitao Du, Richard Yi Da Xu
2021	—	`—`	Understanding deep learning through ultra-wide neural networks Wei Huang
2021	J	`jnl`	Wide Graph Neural Networks: Aggregation Provably Leads to Exponentially Trainability Loss. CoRR Wei Huang, Yayong Li, Weitao Du, Richard Yi Da Xu, Jie Yin, Ling Chen
2020	J	`jnl`	Implicit bias of deep linear networks in the large learning rate phase. CoRR Wei Huang, Weitao Du, Richard Yi Da Xu, Chunrui Liu
2020	A	`conf`	Mean Field Theory for Deep Dropout Networks: Digging up Gradient Backpropagation Deeply. ECAI Wei Huang, Richard Yi Da Xu, Weitao Du, Yutian Zeng, Yunce Zhao
2020	J	`jnl`	On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization. CoRR Wei Huang, Weitao Du, Richard Yi Da Xu
2019	J	`jnl`	Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems. CoRR Wei Huang, Richard Yi Da Xu
2019	J	`jnl`	Mean field theory for deep dropout networks: digging up gradient backpropagation deeply. CoRR Wei Huang, Richard Yi Da Xu, Weitao Du, Yutian Zeng, Yunce Zhao