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ORCIDYatao Bian
Person information
- affiliation: Tencent AI Lab, China
- affiliation: ETH Zürich, Department of Computer Science, Switzerland
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2020 – today
- 2022
[j2]Hehuan Ma
, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye, Junzhou Huang:
Cross-dependent graph neural networks for molecular property prediction. Bioinform. 38(7): 2003-2009 (2022)- [c25]Weizhi An, Yuzhi Guo, Yatao Bian, Hehuan Ma, Jinyu Yang, Chunyuan Li, Junzhou Huang:
MoDNA: motif-oriented pre-training for DNA language model. BCB 2022: 5:1-5:5 - [c24]Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang:
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning. ICLR 2022 - [c23]Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause:
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. ICLR 2022 - [c22]Guoji Fu, Peilin Zhao, Yatao Bian:
p-Laplacian Based Graph Neural Networks. ICML 2022: 6878-6917 - [c21]Jiying Zhang, Xi Xiao, Long-Kai Huang, Yu Rong, Yatao Bian:
Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport. IJCAI 2022: 3730-3736 - [c20]Bingzhe Wu, Yatao Bian, Hengtong Zhang, Jintang Li, Junchi Yu, Liang Chen, Chaochao Chen, Junzhou Huang:
Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. KDD 2022: 4838-4839 - [c19]Lu Zhang, Yang Wang, Jiaogen Zhou, Chenbo Zhang, Yinglu Zhang, Jihong Guan, Yatao Bian, Shuigeng Zhou:
Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method. ACM Multimedia 2022: 2002-2011 - [c18]Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Da Luo, Kangyi Lin, Junzhou Huang, Sophia Ananiadou, Peilin Zhao:
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer. SIGIR 2022: 353-362 - [c17]Erxue Min, Yu Rong, Yatao Bian, Tingyang Xu, Peilin Zhao, Junzhou Huang, Sophia Ananiadou:
Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media. WWW 2022: 1148-1158 - [i37]Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, Yatao Bian:
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery - A Focus on Affinity Prediction Problems with Noise Annotations. CoRR abs/2201.09637 (2022) - [i36]Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, Sophia Ananiadou:
Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction. CoRR abs/2201.13311 (2022) - [i35]Bingzhe Wu, Jintang Li, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang:
Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift. CoRR abs/2202.07114 (2022) - [i34]Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, Yu Rong:
Transformer for Graphs: An Overview from Architecture Perspective. CoRR abs/2202.08455 (2022) - [i33]Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian:
Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference. CoRR abs/2203.01693 (2022) - [i32]Jiying Zhang, Xi Xiao, Long-Kai Huang, Yu Rong, Yatao Bian:
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport. CoRR abs/2203.10453 (2022) - [i31]Jiying Zhang, Fuyang Li, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, Yatao Bian:
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs. CoRR abs/2203.16939 (2022) - [i30]Bingzhe Wu, Zhipeng Liang, Yuxuan Han, Yatao Bian, Peilin Zhao, Junzhou Huang:
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup. CoRR abs/2204.07742 (2022) - [i29]Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao:
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR abs/2205.10014 (2022) - [i28]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng:
Pareto Invariant Risk Minimization. CoRR abs/2206.07766 (2022) - [i27]Xi Leng, Xiaoying Tang, Yatao Bian:
Diversity Boosted Learning for Domain Generalization with Large Number of Domains. CoRR abs/2207.13865 (2022) - [i26]Ziqiao Zhang, Yatao Bian, Ailin Xie, Pengju Han, Long-Kai Huang, Shuigeng Zhou:
Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery? CoRR abs/2209.07423 (2022) - [i25]Lanqing Li, Liang Zeng, Ziqi Gao, Shen Yuan, Yatao Bian, Bingzhe Wu, Hengtong Zhang, Chan Lu, Yang Yu, Wei Liu, Hongteng Xu, Jia Li, Peilin Zhao, Pheng-Ann Heng:
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery. CoRR abs/2209.07921 (2022) - [i24]Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao:
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup. CoRR abs/2209.08928 (2022) - [i23]Lu Zhang, Yang Wang, Jiaogen Zhou, Chenbo Zhang, Yinglu Zhang, Jihong Guan, Yatao Bian, Shuigeng Zhou:
Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method. CoRR abs/2210.03940 (2022) - 2021
- [c16]Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He:
Graph Information Bottleneck for Subgraph Recognition. ICLR 2021 - [c15]Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang:
On Self-Distilling Graph Neural Network. IJCAI 2021: 2278-2284 - [c14]Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu:
Not All Low-Pass Filters are Robust in Graph Convolutional Networks. NeurIPS 2021: 25058-25071 - [i22]Yuzhao Chen, Yatao Bian, Jiying Zhang, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang:
Diversified Multiscale Graph Learning with Graph Self-Correction. CoRR abs/2103.09754 (2021) - [i21]Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He:
Recognizing Predictive Substructures with Subgraph Information Bottleneck. CoRR abs/2103.11155 (2021) - [i20]Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang:
Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations. CoRR abs/2106.02938 (2021) - [i19]Guoji Fu, Peilin Zhao, Yatao Bian:
p-Laplacian Based Graph Neural Networks. CoRR abs/2111.07337 (2021) - [i18]Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause:
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. CoRR abs/2111.07786 (2021) - [i17]Bingzhe Wu, Zhicong Liang, Yatao Bian, Chaochao Chen, Junzhou Huang, Yuan Yao:
Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis. CoRR abs/2112.08439 (2021) - 2020
- [c13]Aytunc Sahin, Yatao Bian, Joachim M. Buhmann, Andreas Krause:
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models. ICML 2020: 8388-8397 - [c12]Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang:
Self-Supervised Graph Transformer on Large-Scale Molecular Data. NeurIPS 2020 - [i16]Hehuan Ma, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye, Junzhou Huang:
Dual Message Passing Neural Network for Molecular Property Prediction. CoRR abs/2005.13607 (2020) - [i15]Aytunc Sahin, Yatao Bian, Joachim M. Buhmann, Andreas Krause:
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models. CoRR abs/2006.01293 (2020) - [i14]Yatao Bian, Joachim M. Buhmann, Andreas Krause:
Continuous Submodular Function Maximization. CoRR abs/2006.13474 (2020) - [i13]Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, Junzhou Huang:
GROVER: Self-supervised Message Passing Transformer on Large-scale Molecular Data. CoRR abs/2007.02835 (2020) - [i12]Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He:
Graph Information Bottleneck for Subgraph Recognition. CoRR abs/2010.05563 (2020) - [i11]Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang:
On Self-Distilling Graph Neural Network. CoRR abs/2011.02255 (2020)
2010 – 2019
- 2019
- [b1]Yatao Bian:
Provable Non-Convex Optimization and Algorithm Validation via Submodularity. ETH Zurich, Zürich, Switzerland, 2019 - [j1]Yatao An Bian, Xiong Li, Yuncai Liu, Ming-Hsuan Yang:
Parallel Coordinate Descent Newton Method for Efficient L1 -Regularized Loss Minimization. IEEE Trans. Neural Networks Learn. Syst. 30(11): 3233-3245 (2019) - [c11]Yatao An Bian, Joachim M. Buhmann, Andreas Krause:
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference. ICML 2019: 644-653 - [i10]Yatao An Bian:
Provable Non-Convex Optimization and Algorithm Validation via Submodularity. CoRR abs/1912.08495 (2019) - 2018
- [c10]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. ICML 2018: 1357-1365 - [c9]Lie He, An Bian, Martin Jaggi:
COLA: Decentralized Linear Learning. NeurIPS 2018: 4541-4551 - [i9]An Bian, Joachim M. Buhmann, Andreas Krause:
Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference. CoRR abs/1805.07482 (2018) - [i8]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. CoRR abs/1806.07569 (2018) - [i7]Lie He, An Bian, Martin Jaggi:
COLA: Communication-Efficient Decentralized Linear Learning. CoRR abs/1808.04883 (2018) - 2017
- [c8]Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause:
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. AISTATS 2017: 111-120 - [c7]Nico S. Gorbach, Andrew An Bian, Benjamin Fischer, Stefan Bauer, Joachim M. Buhmann:
Model Selection for Gaussian Process Regression. GCPR 2017: 306-318 - [c6]Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek:
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. ICML 2017: 498-507 - [c5]An Bian, Kfir Yehuda Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. NIPS 2017: 486-496 - [i6]Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek:
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. CoRR abs/1703.02100 (2017) - [i5]An Bian, Kfir Y. Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. CoRR abs/1711.02515 (2017) - 2016
- [c4]Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann:
Information-theoretic analysis of MaxCut algorithms. ITA 2016: 1-5 - [i4]Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause:
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. CoRR abs/1606.05615 (2016) - [i3]Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann:
Greedy MAXCUT Algorithms and their Information Content. CoRR abs/1609.00810 (2016) - 2015
- [c3]Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann:
Greedy MaxCut algorithms and their information content. ITW 2015: 1-5 - 2013
- [c2]Yatao Bian, Xiong Li, Mingqi Cao, Yuncai Liu:
Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression. ECML/PKDD (3) 2013: 81-95 - [i2]An Bian, Xiong Li, Yuncai Liu:
Parallel Coordinate Descent Newton for Large-scale L1-Regularized Minimization. CoRR abs/1306.4080 (2013) - [i1]Jian Song, Yatao Bian, Junchi Yan, Xu Zhao, Yuncai Liu:
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. CoRR abs/1311.6811 (2013) - 2012
- [c1]Yatao Bian, Xu Zhao, Jian Song, Yuncai Liu:
Parallelized Annealed Particle Filter for real-time marker-less motion tracking via heterogeneous computing. ICPR 2012: 2444-2447
Coauthor Index
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