Huang, Yu

28 publications

ICLR 2025 A Theoretical Analysis of Self-Supervised Learning for Vision Transformers Yu Huang, Zixin Wen, Yuejie Chi, Yingbin Liang

JMLR 2025 ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Christopher Bretherton, Veronika Eyring, Savannah Ferretti, Nicholas Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan M. Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael Pritchard

CVPR 2025 Domain Generalization in CLIP via Learning with Diverse Text Prompts Changsong Wen, Zelin Peng, Yu Huang, Xiaokang Yang, Wei Shen

AAAI 2025 Exploit Your Latents: Coarse-Grained Protein Backmapping with Latent Diffusion Models Rongchao Zhang, Yu Huang, Yiwei Lou, Yi Xin, Haixu Chen, Yongzhi Cao, Hanpin Wang

CVPR 2025 MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal Output Yanyuan Chen, Dexuan Xu, Yu Huang, Songkun Zhan, Hanpin Wang, Dongxue Chen, Xueping Wang, Meikang Qiu, Hang Li

NeurIPS 2025 MoleBridge: Synthetic Space Projecting with Discrete Markov Bridges Rongchao Zhang, Yu Huang, Yongzhi Cao, Hanpin Wang

NeurIPS 2025 Multi-Head Transformers Provably Learn Symbolic Multi-Step Reasoning via Gradient Descent Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi

CVPR 2025 Parameter-Efficient Fine-Tuning in Hyperspherical Space for Open-Vocabulary Semantic Segmentation Zelin Peng, Zhengqin Xu, Zhilin Zeng, Yu Huang, Yaoming Wang, Wei Shen

TMLR 2025 Pre-Training Representations of Binary Code Using Contrastive Learning Yifan Zhang, Chen Huang, Yueke Zhang, Huajie Shao, Kevin Leach, Yu Huang

AAAI 2025 STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counseling Jieyi Wang, Yue Huang, Zeming Liu, Dexuan Xu, Chuan Wang, Xiaoming Shi, Ruiyuan Guan, Hongxing Wang, Weihua Yue, Yu Huang

CVPR 2025 Star with Bilinear Mapping Zelin Peng, Yu Huang, Zhengqin Xu, Feilong Tang, Ming Hu, Xiaokang Yang, Wei Shen

NeurIPS 2025 Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization Yu Huang, Zixin Wen, Aarti Singh, Yuejie Chi, Yuxin Chen

CVPR 2025 Understanding Fine-Tuning CLIP for Open-Vocabulary Semantic Segmentation in Hyperbolic Space Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, Wei Shen

AAAI 2024 A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis Rongchao Zhang, Yiwei Lou, Dexuan Xu, Yongzhi Cao, Hanpin Wang, Yu Huang

ICML 2024 Accelerating Convergence of Score-Based Diffusion Models, Provably Gen Li, Yu Huang, Timofey Efimov, Yuting Wei, Yuejie Chi, Yuxin Chen

ICML 2024 BetterV: Controlled Verilog Generation with Discriminative Guidance Zehua Pei, Huiling Zhen, Mingxuan Yuan, Yu Huang, Bei Yu

ICMLW 2024 How Transformers Learn Diverse Attention Correlations in Masked Vision Pretraining Yu Huang, Zixin Wen, Yuejie Chi, Yingbin Liang

ICML 2024 In-Context Convergence of Transformers Yu Huang, Yuan Cheng, Yingbin Liang

NeurIPS 2024 In-Context Learning with Representations: Contextual Generalization of Trained Transformers Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi

ICMLW 2024 In-Context Learning with Representations: Contextual Generalization of Trained Transformers Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi

NeurIPS 2023 ClimSim: A Large Multi-Scale Dataset for Hybrid Physics-ML Climate Emulation Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce Harrop, Benjamin Hillman, Andrea Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David Randall, Sara Shamekh, Mark Taylor, Nathan Urban, Janni Yuval, Guang Zhang, Mike Pritchard

NeurIPSW 2023 In-Context Convergence of Transformers Yu Huang, Yuan Cheng, Yingbin Liang

TMLR 2023 Online Min-Max Problems with Non-Convexity and Non-Stationarity Yu Huang, Yuan Cheng, Yingbin Liang, Longbo Huang

ICML 2022 Modality Competition: What Makes Joint Training of Multi-Modal Network Fail in Deep Learning? (Provably) Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang

NeurIPSW 2022 Online Min-Max Optimization: Nonconvexity, Nonstationarity, and Dynamic Regret Yu Huang, Yuan Cheng, Yingbin Liang, Longbo Huang

NeurIPS 2022 Provable Generalization of Overparameterized Meta-Learning Trained with SGD Yu Huang, Yingbin Liang, Longbo Huang

IJCAI 2021 GAEN: Graph Attention Evolving Networks Min Shi, Yu Huang, Xingquan Zhu, Yufei Tang, Yuan Zhuang, Jianxun Liu

NeurIPS 2021 What Makes Multi-Modal Learning Better than Single (Provably) Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen, Hang Zhao, Longbo Huang