Bian, Yatao

45 publications

NeurIPS 2025 3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-Mask Decoding Chang Wu, Zhiyuan Liu, Wen Shu, Liang Wang, Yanchen Luo, Wenqiang Lei, Yatao Bian, Junfeng Fang, Xiang Wang
ICLR 2025 COME: Test-Time Adaption by Conservatively Minimizing Entropy Qingyang Zhang, Yatao Bian, Xinke Kong, Peilin Zhao, Changqing Zhang
ICLR 2025 Erasing Concept Combination from Text-to-Image Diffusion Model Hongyi Nie, Quanming Yao, Yang Liu, Zhen Wang, Yatao Bian
ICML 2025 Hierarchical Graph Tokenization for Molecule-Language Alignment Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian
ICLR 2025 InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization Yifan Niu, Ziqi Gao, Tingyang Xu, Yang Liu, Yatao Bian, Yu Rong, Junzhou Huang, Jia Li
NeurIPS 2025 Learning to Learn with Contrastive Meta-Objective Shiguang Wu, Yaqing Wang, Yatao Bian, Quanming Yao
ICML 2025 Measuring Diversity in Synthetic Datasets Yuchang Zhu, Huizhe Zhang, Bingzhe Wu, Jintang Li, Zibin Zheng, Peilin Zhao, Liang Chen, Yatao Bian
ICLRW 2025 Rich Feature Learning via Diversification Xi Leng, Yongqiang Chen, Xiaoying Tang, Yatao Bian
NeurIPS 2025 Right Question Is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization Qingyang Zhang, Haitao Wu, Changqing Zhang, Peilin Zhao, Yatao Bian
ICLRW 2025 UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation Shuhan Guo, Yatao Bian, Ruibing Wang, Nan Yin, Quanming Yao
IJCAI 2025 Unified Molecule-Text Language Model with Discrete Token Representation Shuhan Guo, Yatao Bian, Ruibing Wang, Nan Yin, Zhen Wang, Quanming Yao
ICLR 2024 EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model Huaijin Wu, Wei Liu, Yatao Bian, Jiaxiang Wu, Nianzu Yang, Junchi Yan
ICLR 2024 Enhancing Neural Subset Selection: Integrating Background Information into Set Representations Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng
ICML 2024 How Interpretable Are Interpretable Graph Neural Networks? Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
ICMLW 2024 Improving Graph-Language Alignment with Hierarchical Graph Tokenization Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian
ICLRW 2024 Interpretable and Generalizable Graph Learning via Subgraph Multilinear Extension Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
ICLRW 2024 Simple Permutations Can Fool Llama: Permutation Attack and Defense for Large Language Models Liang Chen, Yatao Bian, Li Shen, Kam-Fai Wong
ICMLW 2024 Step-on-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao
NeurIPS 2024 The Best of Both Worlds: On the Dilemma of Out-of-Distribution Detection Qingyang Zhang, Qiuxuan Feng, Joey Tianyi Zhou, Yatao Bian, Qinghua Hu, Changqing Zhang
ICLRW 2024 WatME: Towards Lossless Watermarking Through Lexical Redundancy Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong
ICLR 2023 BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion Fu-Yun Wang, Da-Wei Zhou, Liu Liu, Han-Jia Ye, Yatao Bian, De-Chuan Zhan, Peilin Zhao
NeurIPS 2023 Does Invariant Graph Learning via Environment Augmentation Learn Invariance? Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng
AAAI 2023 DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery - A Focus on Affinity Prediction Problems with Noise Annotations Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Lanqing Li, Long-Kai Huang, Tingyang Xu, Yu Rong, Jie Ren, Ding Xue, Houtim Lai, Wei Liu, Junzhou Huang, Shuigeng Zhou, Ping Luo, Peilin Zhao, Yatao Bian
NeurIPS 2023 Fairness-Guided Few-Shot Prompting for Large Language Models Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, Bingzhe Wu
NeurIPS 2023 Learning Invariant Molecular Representation in Latent Discrete Space Xiang Zhuang, Qiang Zhang, Keyan Ding, Yatao Bian, Xiao Wang, Jingsong Lv, Hongyang Chen, Huajun Chen
ICLR 2023 Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Ma Kaili, Han Yang, Peilin Zhao, Bo Han, James Cheng
NeurIPS 2023 SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan
NeurIPS 2023 Understanding and Improving Feature Learning for Out-of-Distribution Generalization Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng
ICML 2022 $p$-Laplacian Based Graph Neural Networks Guoji Fu, Peilin Zhao, Yatao Bian
ICLRW 2022 Diversified Multiscale Graph Learning with Graph Self-Correction Yuzhao Chen, Yatao Bian, Jiying Zhang, Xi Xiao, Tingyang Xu, Yu Rong
NeurIPSW 2022 Diversity Boosted Learning for Domain Generalization with a Large Number of Domains Xi Leng, Yatao Bian, Xiaoying Tang
ICLR 2022 Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang
IJCAI 2022 Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport Jiying Zhang, Xi Xiao, Long-Kai Huang, Yu Rong, Yatao Bian
ICLR 2022 Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause
ICMLW 2022 Invariance Principle Meets Out-of-Distribution Generalization on Graphs Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng
NeurIPS 2022 Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Ma Kaili, Binghui Xie, Tongliang Liu, Bo Han, James Cheng
NeurIPS 2022 Learning Neural Set Functions Under the Optimal Subset Oracle Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian
NeurIPS 2022 UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao
ICLR 2021 Graph Information Bottleneck for Subgraph Recognition Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He
NeurIPS 2021 Not All Low-Pass Filters Are Robust in Graph Convolutional Networks Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu
IJCAI 2021 On Self-Distilling Graph Neural Network Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang
ICML 2020 From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models Aytunc Sahin, Yatao Bian, Joachim Buhmann, Andreas Krause
NeurIPS 2020 Self-Supervised Graph Transformer on Large-Scale Molecular Data Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang
ICML 2019 Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference Yatao Bian, Joachim Buhmann, Andreas Krause
ECML-PKDD 2013 Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression Yatao Bian, Xiong Li, Mingqi Cao, Yuncai Liu