Parallelized Annealed Particle Filter for real-time marker-less motion tracking via heterogeneous computing. - Yatao Bian; Xu Zhao 001; Jian Song; Yuncai Liu (ICPR, 2012)

Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression. - Yatao Bian; Xiong Li 004; Mingqi Cao; Yuncai Liu (ECML/PKDD (3), 2013)

Parallel Coordinate Descent Newton for Large-scale L1-Regularized Minimization. - An Bian; Xiong Li 004; Yuncai Liu (CoRR, 2013)

Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel Reconstruction and Marker-less Motion Tracking via GPU Acceleration. - Jian Song; Yatao Bian; Junchi Yan; Xu Zhao 001; Yuncai Liu (CoRR, 2013)

Greedy MaxCut algorithms and their information content. - Yatao Bian; Alexey Gronskiy; Joachim M. Buhmann (ITW, 2015)

Information-theoretic analysis of MaxCut algorithms. - Yatao Bian; Alexey Gronskiy; Joachim M. Buhmann (ITA, 2016)

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. - Andrew An Bian; Baharan Mirzasoleiman; Joachim M. Buhmann; Andreas Krause 001 (AISTATS, 2017)

Model Selection for Gaussian Process Regression. - Nico S. Gorbach; Andrew An Bian; Benjamin Fischer; Stefan Bauer; Joachim M. Buhmann (GCPR, 2017)

Guarantees for Greedy Maximization of Non-submodular Functions with Applications. - Andrew An Bian; Joachim M. Buhmann; Andreas Krause 001; Sebastian Tschiatschek (ICML, 2017)

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. - An Bian; Kfir Yehuda Levy; Andreas Krause 001; Joachim M. Buhmann (NIPS, 2017)

A Distributed Second-Order Algorithm You Can Trust. - Celestine Dünner; Aurélien Lucchi; Matilde Gargiani; An Bian; Thomas Hofmann 001; Martin Jaggi (ICML, 2018)

COLA: Decentralized Linear Learning. - Lie He; An Bian; Martin Jaggi (NeurIPS, 2018)

Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference. - An Bian; Joachim M. Buhmann; Andreas Krause 001 (CoRR, 2018)

COLA: Communication-Efficient Decentralized Linear Learning. - Lie He; An Bian; Martin Jaggi (CoRR, 2018)

Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference. - Yatao An Bian; Joachim M. Buhmann; Andreas Krause 001 (ICML, 2019)

Provable Non-Convex Optimization and Algorithm Validation via Submodularity. - Yatao An Bian (CoRR, 2019)

Parallel Coordinate Descent Newton Method for Efficient L1 -Regularized Loss Minimization. - Yatao An Bian; Xiong Li 004; Yuncai Liu; Ming-Hsuan Yang 001 (IEEE Trans. Neural Networks Learn. Syst., 2019)

Provable Non-Convex Optimization and Algorithm Validation via Submodularity. - Yatao Bian (2019)

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models. - Aytunc Sahin; Yatao Bian; Joachim M. Buhmann; Andreas Krause 001 (ICML, 2020)

Self-Supervised Graph Transformer on Large-Scale Molecular Data. - Yu Rong 001; Yatao Bian; Tingyang Xu; Weiyang Xie; Ying Wei 001; Wenbing Huang 001; Junzhou Huang (NeurIPS, 2020)

Dual Message Passing Neural Network for Molecular Property Prediction. - Hehuan Ma; Yatao Bian; Yu Rong 001; Wenbing Huang 001; Tingyang Xu; Weiyang Xie; Geyan Ye; Junzhou Huang (CoRR, 2020)

Continuous Submodular Function Maximization. - Yatao Bian; Joachim M. Buhmann; Andreas Krause 001 (CoRR, 2020)

GROVER: Self-supervised Message Passing Transformer on Large-scale Molecular Data. - Yu Rong 001; Yatao Bian; Tingyang Xu; Weiyang Xie; Ying Wei 001; Wenbing Huang 001; Junzhou Huang (CoRR, 2020)

Graph Information Bottleneck for Subgraph Recognition. - Junchi Yu; Tingyang Xu; Yu Rong 001; Yatao Bian; Junzhou Huang; Ran He 001 (ICLR, 2021)

On Self-Distilling Graph Neural Network. - Yuzhao Chen; Yatao Bian; Xi Xiao; Yu Rong 001; Tingyang Xu; Junzhou Huang (IJCAI, 2021)

Not All Low-Pass Filters are Robust in Graph Convolutional Networks. - Heng Chang; Yu Rong 001; Tingyang Xu; Yatao Bian; Shiji Zhou; Xin Wang 019; Junzhou Huang; Wenwu Zhu 001 (NeurIPS, 2021)

Diversified Multiscale Graph Learning with Graph Self-Correction. - Yuzhao Chen; Yatao Bian; Jiying Zhang; Xi Xiao; Tingyang Xu; Yu Rong 001; Junzhou Huang (CoRR, 2021)

Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations. - Yatao Bian; Yu Rong 001; Tingyang Xu; Jiaxiang Wu 001; Andreas Krause 001; Junzhou Huang (CoRR, 2021)

Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis. - Bingzhe Wu; Zhicong Liang; Yatao Bian; Chaochao Chen 001; Junzhou Huang; Yuan Yao (CoRR, 2021)

MoDNA: motif-oriented pre-training for DNA language model. - Weizhi An; Yuzhi Guo; Yatao Bian; Hehuan Ma; Jinyu Yang; Chunyuan Li; Junzhou Huang (BCB, 2022)

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning. - Yatao Bian; Yu Rong 001; Tingyang Xu; Jiaxiang Wu 001; Andreas Krause 001; Junzhou Huang (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 001 (ICLR, 2022)

p-Laplacian Based Graph Neural Networks. - Guoji Fu; Peilin Zhao; Yatao Bian (ICML, 2022)

Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport. - Jiying Zhang; Xi Xiao; Long-Kai Huang; Yu Rong 001; Yatao Bian (IJCAI, 2022)

Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. - Bingzhe Wu; Yatao Bian; Hengtong Zhang; Jintang Li; Junchi Yu; Liang Chen 001; Chaochao Chen 001; Junzhou Huang (KDD, 2022)

Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method. - Lu Zhang 060; Yang Wang 100; Jiaogen Zhou; Chenbo Zhang; Yinglu Zhang; Jihong Guan; Yatao Bian; Shuigeng Zhou (ACM Multimedia, 2022)

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. - Yongqiang Chen 002; Yonggang Zhang 003; Yatao Bian; Han Yang 002; Kaili Ma 001; Binghui Xie; Tongliang Liu; Bo Han 003; James Cheng (NeurIPS, 2022)

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup. - Zongbo Han; Zhipeng Liang; Fan Yang 081; Liu Liu 014; Lanqing Li; Yatao Bian; Peilin Zhao; Bingzhe Wu; Changqing Zhang; Jianhua Yao 001 (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)

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer. - Erxue Min; Yu Rong 001; Tingyang Xu; Yatao Bian; Da Luo; Kangyi Lin; Junzhou Huang; Sophia Ananiadou; Peilin Zhao (SIGIR, 2022)

Diversified Multiscale Graph Learning with Graph Self-Correction. - Yuzhao Chen; Yatao Bian; Jiying Zhang; Xi Xiao; Tingyang Xu; Yu Rong 001 (TAG-ML, 2022)

Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media. - Erxue Min; Yu Rong 001; Yatao Bian; Tingyang Xu; Peilin Zhao; Junzhou Huang; Sophia Ananiadou (WWW, 2022)

Cross-dependent graph neural networks for molecular property prediction. - Hehuan Ma; Yatao Bian; Yu Rong 001; Wenbing Huang 001; Tingyang Xu; Weiyang Xie; Geyan Ye; Junzhou Huang (Bioinform., 2022)

DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery - A Focus on Affinity Prediction Problems with Noise Annotations. - Yuanfeng Ji; Lu Zhang 060; Jiaxiang Wu 001; Bingzhe Wu; Long-Kai Huang; Tingyang Xu; Yu Rong 001; Lanqing Li; Jie Ren; Ding Xue; Houtim Lai; Shaoyong Xu; et al. (CoRR, 2022)

Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction. - Erxue Min; Yu Rong 001; Tingyang Xu; Yatao Bian; Peilin Zhao; Junzhou Huang; Da Luo; Kangyi Lin; Sophia Ananiadou (CoRR, 2022)

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift. - Bingzhe Wu; Jintang Li; Chengbin Hou; Guoji Fu; Yatao Bian; Liang Chen 001; Junzhou Huang (CoRR, 2022)

Transformer for Graphs: An Overview from Architecture Perspective. - Erxue Min; Runfa Chen; Yatao Bian; Tingyang Xu; Kangfei Zhao; Wenbing Huang 001; Peilin Zhao; Junzhou Huang; Sophia Ananiadou; Yu Rong 001 (CoRR, 2022)

Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference. - Zijing Ou; Tingyang Xu; Qinliang Su; Yingzhen Li; Peilin Zhao; Yatao Bian (CoRR, 2022)

Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs. - Jiying Zhang; Fuyang Li; Xi Xiao; Tingyang Xu; Yu Rong 001; Junzhou Huang; Yatao Bian (CoRR, 2022)

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup. - Bingzhe Wu; Zhipeng Liang; Yuxuan Han; Yatao Bian; Peilin Zhao; Junzhou Huang (CoRR, 2022)

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. - Bingzhe Wu; Jintang Li; Junchi Yu; Yatao Bian; Hengtong Zhang; Chaochao Chen 001; Chengbin Hou; Guoji Fu; Liang Chen 001; Tingyang Xu; Yu Rong 001; Xiaolin Zheng; et al. (CoRR, 2022)

Pareto Invariant Risk Minimization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Kaili Ma 001; Yonggang Zhang 003; Han Yang 002; Bo Han 003; James Cheng (CoRR, 2022)

Diversity Boosted Learning for Domain Generalization with Large Number of Domains. - Xi Leng; Xiaoying Tang 002; Yatao Bian (CoRR, 2022)

Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery? - Ziqiao Zhang; Yatao Bian; Ailin Xie; Pengju Han; Long-Kai Huang; Shuigeng Zhou (CoRR, 2022)

ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery. - Lanqing Li; Liang Zeng 002; Ziqi Gao; Shen Yuan; Yatao Bian; Bingzhe Wu; Hengtong Zhang; Chan Lu; Yang Yu 010; Wei Liu 005; Hongteng Xu; Jia Li 009; et al. (CoRR, 2022)

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 060; Jiaxiang Wu 001; Bingzhe Wu; Lanqing Li; Long-Kai Huang; Tingyang Xu; Yu Rong 001; Jie Ren; Ding Xue; Houtim Lai; Wei Liu 005; et al. (AAAI, 2023)

SAILOR: Structural Augmentation Based Tail Node Representation Learning. - Jie Liao; Jintang Li; Liang Chen 001; Bingzhe Wu; Yatao Bian; Zibin Zheng (CIKM, 2023)

Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators. - Liang Chen 001; Yang Deng 002; Yatao Bian; Zeyu Qin; Bingzhe Wu; Tat-Seng Chua; Kam-Fai Wong (EMNLP, 2023)

RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data. - Haotian Wang; Zhen Zhang 048; Mengting Hu; Qichao Wang; Liang Chen 001; Yatao Bian; Bingzhe Wu (EMNLP (Findings), 2023)

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Bingzhe Wu; Yonggang Zhang 003; Kaili Ma 001; Han Yang 002; Peilin Zhao; Bo Han 003; James Cheng (ICLR, 2023)

BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion. - Fu-Yun Wang; Da-Wei Zhou 001; Liu Liu 014; Han-Jia Ye; Yatao Bian; De-Chuan Zhan; Peilin Zhao (ICLR, 2023)

Does Invariant Graph Learning via Environment Augmentation Learn Invariance? - Yongqiang Chen 002; Yatao Bian; Kaiwen Zhou; Binghui Xie; Bo Han 003; James Cheng (NeurIPS, 2023)

Understanding and Improving Feature Learning for Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou; Yatao Bian; Bo Han 003; James Cheng (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 013; Huazhu Fu; Qinghua Hu; Bingzhe Wu (NeurIPS, 2023)

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)

Learning Invariant Molecular Representation in Latent Discrete Space. - Xiang Zhuang; Qiang Zhang 026; Keyan Ding; Yatao Bian; Xiao Wang 014; Jingsong Lv; Hongyang Chen; Huajun Chen (NeurIPS, 2023)

Curriculum Graph Poisoning. - Hanwen Liu; Peilin Zhao; Tingyang Xu; Yatao Bian; Junzhou Huang; Yuesheng Zhu; Yadong Mu (WWW, 2023)

Activity Cliff Prediction: Dataset and Benchmark. - Ziqiao Zhang; Bangyi Zhao; Ailin Xie; Yatao Bian; Shuigeng Zhou (CoRR, 2023)

Reweighted Mixup for Subpopulation Shift. - Zongbo Han; Zhipeng Liang; Fan Yang 081; Liu Liu 014; Lanqing Li; Yatao Bian; Peilin Zhao; Qinghua Hu; Bingzhe Wu; Changqing Zhang; Jianhua Yao 001 (CoRR, 2023)

Towards Understanding Feature Learning in Out-of-Distribution Generalization. - Yongqiang Chen 002; Wei Huang; Kaiwen Zhou; Yatao Bian; Bo Han 003; James Cheng (CoRR, 2023)

SyNDock: N Rigid Protein Docking via Learnable Group Synchronization. - Yuanfeng Ji; Yatao Bian; Guoji Fu; Peilin Zhao; Ping Luo 002 (CoRR, 2023)

ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking. - Yiqiang Yi; Xu Wan; Yatao Bian; Le Ou-Yang; Peilin Zhao (CoRR, 2023)

X-Mark: Towards Lossless Watermarking Through Lexical Redundancy. - Liang Chen 001; Yatao Bian; Yang Deng 002; Shuaiyi Li; Bingzhe Wu; Peilin Zhao; Kam-Fai Wong (CoRR, 2023)

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. - Yongqiang Chen 002; Binghui Xie; Kaiwen Zhou; Bo Han 003; Yatao Bian; James Cheng (CoRR, 2023)

WatME: Towards Lossless Watermarking Through Lexical Redundancy. - Liang Chen 001; Yatao Bian; Yang Deng 002; Deng Cai 002; Shuaiyi Li; Peilin Zhao; Kam-Fai Wong (ACL (1), 2024)

EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model. - Huaijin Wu; Wei Liu 005; Yatao Bian; Jiaxiang Wu 001; Nianzu Yang; Junchi Yan (ICLR, 2024)

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. - Binghui Xie; Yatao Bian; Kaiwen Zhou; Yongqiang Chen 002; Peilin Zhao; Bo Han 003; Wei Meng 001; James Cheng (ICLR, 2024)

How Interpretable Are Interpretable Graph Neural Networks? - Yongqiang Chen 002; Yatao Bian; Bo Han 003; James Cheng (ICML, 2024)

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. - Yuchang Zhu; Jintang Li; Yatao Bian; Zibin Zheng; Liang Chen 001 (KDD, 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 (NeurIPS, 2024)

Rethinking and Simplifying Bootstrapped Graph Latents. - Wangbin Sun; Jintang Li; Liang Chen 001; Bingzhe Wu; Yatao Bian; Zibin Zheng (WSDM, 2024)

Integration of cognitive tasks into artificial general intelligence test for large models. - Youzhi Qu; Chen Wei 006; Penghui Du; Wenxin Che; Chi Zhang; Wanli Ouyang; Yatao Bian; Feiyang Xu; Bin Hu 001; Kai Du; Haiyan Wu; Jia Liu; et al. (CoRR, 2024)

Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping. - Haoyu Wang 003; Guozheng Ma; Ziqiao Meng; Zeyu Qin; Li Shen 008; Zhong Zhang; Bingzhe Wu; Liu Liu 014; Yatao Bian; Tingyang Xu; Xueqian Wang 001; Peilin Zhao (CoRR, 2024)

Graph Unitary Message Passing. - Haiquan Qiu; Yatao Bian; Quanming Yao (CoRR, 2024)

HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment. - Yongqiang Chen 002; Quanming Yao; Juzheng Zhang; James Cheng; Yatao Bian (CoRR, 2024)

UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation. - Juzheng Zhang; Yatao Bian; Yongqiang Chen 002; Quanming Yao (CoRR, 2024)

Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation. - Haoyu Wang 003; Bingzhe Wu; Yatao Bian; Yongzhe Chang; Xueqian Wang 001; Peilin Zhao (CoRR, 2024)

ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning. - Shiguang Wu 002; Yaqing Wang 002; Yatao Bian; Quanming Yao (CoRR, 2024)

Can Pretrained Models Really Learn Better Molecular Representations for AI-Aided Drug Discovery? - Ziqiao Zhang; Yatao Bian; Ailin Xie; Pengju Han; Shuigeng Zhou (J. Chem. Inf. Model., 2024)

Recognizing Predictive Substructures With Subgraph Information Bottleneck. - Junchi Yu; Tingyang Xu; Yu Rong 001; Yatao Bian; Junzhou Huang; Ran He 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection. - Yifan He 005; Yatao Bian; Xi Ding; Bingzhe Wu; Jihong Guan; Ji Zhang 001; Shuigeng Zhou (ACM Trans. Knowl. Discov. Data, 2024)

RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning. - Yifan Niu; Miao Peng; Nuo Chen 001; Yatao Bian; Tingyang Xu; Jia Li 009 (ACL (Findings), 2025)

Erasing Concept Combination from Text-to-Image Diffusion Model. - Hongyi Nie; Quanming Yao; Yang Liu; Zhen Wang 004; Yatao Bian (ICLR, 2025)

InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization. - Yifan Niu; Ziqi Gao; Tingyang Xu; Yang Liu 165; Yatao Bian; Yu Rong 001; Junzhou Huang; Jia Li 009 (ICLR, 2025)

COME: Test-time Adaption by Conservatively Minimizing Entropy. - Qingyang Zhang; Yatao Bian; Xinke Kong; Peilin Zhao; Changqing Zhang (ICLR, 2025)

Measuring Diversity in Synthetic Datasets. - Yuchang Zhu; Huizhe Zhang; Bingzhe Wu; Jintang Li; Zibin Zheng; Peilin Zhao; Liang Chen 001; Yatao Bian (CoRR, 2025)

Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization. - Qingyang Zhang; Haitao Wu; Changqing Zhang; Peilin Zhao; Yatao Bian (CoRR, 2025)

Learning hierarchical time-frequency representation for long-term time series forecasting. - Zhongju Wang; Zhenhong Sun; Yatao Bian; Huadong Mo; Daoyi Dong (Inf. Process. Manag., 2026)