Yatao Bian

Yatao Bian, Junzhou Huang, Jinyu Yang, 2022, Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics.

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Yatao Bian, P. Zhao, Fan Yang, 2022, 2209.08928.

Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method

Yatao Bian, Lu Zhang, J. Guan, 2022, ACM Multimedia.

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

Yatao Bian, Tongliang Liu, Han Yang, 2022, NeurIPS.

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Yatao Bian, P. Zhao, Zongbo Han, 2022, ArXiv.

ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery

Yatao Bian, P. Zhao, P. Heng, 2022, ArXiv.

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

Yatao Bian, Junzhou Huang, P. Zhao, 2022, SIGIR.

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Yatao Bian, P. Zhao, Zongbo Han, 2022, Neural Information Processing Systems.

Diversity Boosted Learning for Domain Generalization with Large Number of Domains

Yatao Bian, Xiaoying Tang, Xinlin Leng, 2022, ArXiv.

Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

Yatao Bian, Zibin Zheng, Junzhou Huang, 2022 .

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

T. Jaakkola, R. Barzilay, Yatao Bian, 2021, ICLR.

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

T. Jaakkola, R. Barzilay, Yatao Bian, 2021, ICLR.

DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery - A Focus on Affinity Prediction Problems with Noise Annotations

Junzhou Huang, Yu Rong, Shuigeng Zhou, 2022, ArXiv.

Multi-View Graph Neural Networks for Molecular Property Prediction

Wenbing Huang, Junzhou Huang, Yu Rong, 2020, 2005.13607.

Graph Information Bottleneck for Subgraph Recognition pdf

Yu Rong, Tingyang Xu, Ran He, 2020, ICLR.

Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Yatao Bian, Junzhou Huang, Bingzhe Wu, 2022, KDD.

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization

Yatao Bian, P. Zhao, Han Yang, 2022, ICLR.

Towards Understanding Feature Learning in Out-of-Distribution Generalization

Yatao Bian, Wei Huang, Kaiwen Zhou, 2023, ArXiv.

Guarantees for Greedy Maximization of Non-submodular Functions with Applications pdf

Andreas Krause, Joachim M. Buhmann, Sebastian Tschiatschek, 2017, ICML.

Reweighted Mixup for Subpopulation Shift

Yatao Bian, P. Zhao, Zongbo Han, 2023, ArXiv.

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup

Yatao Bian, Junzhou Huang, P. Zhao, 2022, ArXiv.

Fairness-guided Few-shot Prompting for Large Language Models

Yatao Bian, P. Zhao, Zhirui Zhang, 2023, ArXiv.

On Self-Distilling Graph Neural Network

Yatao Bian, Junzhou Huang, Xi Xiao, 2020, IJCAI.

Recognizing Predictive Substructures With Subgraph Information Bottleneck

Junzhou Huang, Ran He, Yu Rong, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference

Yatao Bian, Yingzhen Li, P. Zhao, 2022, ArXiv.

Transformer for Graphs: An Overview from Architecture Perspective

Yatao Bian, Junzhou Huang, P. Zhao, 2022, ArXiv.

Curriculum Graph Poisoning

Yatao Bian, Junzhou Huang, Yadong Mu, 2023, WWW.

Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

Yatao Bian, Zibin Zheng, Junzhou Huang, 2022, 2202.07114.

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift

Yatao Bian, Junzhou Huang, Bingzhe Wu, 2022, ArXiv.

SyNDock: N Rigid Protein Docking via Learnable Group Synchronization

Yatao Bian, Ping Luo, Guoji Fu, 2023, ArXiv.

Learning Neural Set Functions Under the Optimal Subset Oracle

Yatao Bian, Yingzhen Li, P. Zhao, 2022, NeurIPS.

Continuous Submodular Function Maximization pdf

Joachim M. Buhmann, Andreas Krause, Yatao Bian, 2020, ArXiv.

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Joachim M. Buhmann, Andreas Krause, Aytunc Sahin, 2020, ICML.

Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference

Andreas Krause, Joachim M. Buhmann, Yatao A. Bian, 2019, ICML.

Regularization-based methods impose constraints when learning tasks

Fu Lee Wang, Yatao Bian, Han-Jia Ye, 2023 .

Dual Message Passing Neural Network for Molecular Property Prediction pdf

Junzhou Huang, Yu Rong, Tingyang Xu, 2020, ArXiv.

GROVER: Self-supervised Message Passing Transformer on Large-scale Molecular Data pdf

Wenbing Huang, Junzhou Huang, Yu Rong, 2020, ArXiv.

Activity Cliff Prediction: Dataset and Benchmark

Yatao Bian, Shuigeng Zhou, Bangyi Zhao, 2023, ArXiv.

Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?

Yatao Bian, Shuigeng Zhou, Peng Han, 2022, ArXiv.

Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport

Yatao Bian, Yu Rong, Xi Xiao, 2022, IJCAI.

Diversified Multiscale Graph Learning with Graph Self-Correction pdf

Junzhou Huang, Xi Xiao, Yu Rong, 2021, TAG-ML.

D IVERSIFIED M ULTISCALE G RAPH L EARNING WITH G RAPH S ELF -C ORRECTION

Yatao Bian, Xi Xiao, Yu Rong, 2022 .

Simplifying and Empowering Transformers for Large-Graph Representations

Junchi Yan, Yatao Bian, Qitian Wu, 2023, ArXiv.

Continuous DR-submodular Maximization: Structure and Algorithms

Andreas Krause, Joachim M. Buhmann, Kfir Y. Levy, 2017, NIPS 2017.

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains

Andreas Krause, Joachim M. Buhmann, Baharan Mirzasoleiman, 2016, AISTATS.

Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction

Yatao Bian, Junzhou Huang, P. Zhao, 2022, ArXiv.

DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery - a Focus on Affinity Prediction Problems with Noise Annotations

Yatao Bian, Jiaxiang Wu, Junzhou Huang, 2023, AAAI.

Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression

Xiong Li, Yuncai Liu, Yatao Bian, 2013, ECML/PKDD.

Parallel Coordinate Descent Newton for Large-scale L1-Regularized Minimization pdf

Xiong Li, Yuncai Liu, Yatao Bian, 2013, arXiv.org.

SAILOR: Structural Augmentation Based Tail Node Representation Learning

Yatao Bian, Zibin Zheng, Liang Chen, 2023, CIKM.

Provable Non-Convex Optimization and Algorithm Validation via Submodularity pdf

Yatao An Bian, Yatao Bian, 2019, ArXiv.

Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis pdf

Junzhou Huang, Yatao Bian, Bingzhe Wu, 2021, ArXiv.

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

Yatao Bian, Junzhou Huang, P. Zhao, 2022, SIGIR.

Information-theoretic analysis of MaxCut algorithms

Joachim M. Buhmann, Alexey Gronskiy, Yatao Bian, 2016, 2016 Information Theory and Applications Workshop (ITA).

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning

Junzhou Huang, Yu Rong, Andreas Krause, 2021, International Conference on Learning Representations.

BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion

Fu Lee Wang, Yatao Bian, Han-Jia Ye, 2023, ICLR.

Understanding and Improving Feature Learning for Out-of-Distribution Generalization

Yatao Bian, Wei Huang, Kaiwen Zhou, 2023, NeurIPS.

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning

Yatao Bian, Jiaxiang Wu, Junzhou Huang, 2021, ICLR.

Simplifying and Empowering Transformers for Large-Graph Representations

Junchi Yan, Yatao Bian, Qitian Wu, 2023, NeurIPS.