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ORCIDYatao Bian
Yatao An Bian – An Bian – Andrew An Bian
Person information
- affiliation: Tencent AI Lab, China
- affiliation: ETH Zürich, Department of Computer Science, Switzerland
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2020 – today
- 2024
[j5]Ziqiao Zhang
, Yatao Bian, Ailin Xie, Pengju Han, Shuigeng Zhou:
Can Pretrained Models Really Learn Better Molecular Representations for AI-Aided Drug Discovery? J. Chem. Inf. Model. 64(7): 2921-2930 (2024)- [j4]Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He:
Recognizing Predictive Substructures With Subgraph Information Bottleneck. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1650-1663 (2024) - [j3]Yifan He, Yatao Bian, Xi Ding, Bingzhe Wu, Jihong Guan, Ji Zhang, Shuigeng Zhou:
Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection. ACM Trans. Knowl. Discov. Data 18(8): 187:1-187:24 (2024) - [c47]Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong:
WatME: Towards Lossless Watermarking Through Lexical Redundancy. ACL (1) 2024: 9166-9180 - [c46]Huaijin Wu, Wei Liu, Yatao Bian, Jiaxiang Wu, Nianzu Yang, Junchi Yan:
EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model. ICLR 2024 - [c45]Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng:
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. ICLR 2024 - [c44]Yongqiang Chen, Yatao Bian, Bo Han, James Cheng:
How Interpretable Are Interpretable Graph Neural Networks? ICML 2024 - [c43]Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen:
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. KDD 2024: 4688-4699 - [c42]Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
Rethinking and Simplifying Bootstrapped Graph Latents. WSDM 2024: 665-673 - [i63]Youzhi Qu, Chen Wei, Penghui Du, Wenxin Che, Chi Zhang, Wanli Ouyang, Yatao Bian, Feiyang Xu, Bin Hu, Kai Du, Haiyan Wu, Jia Liu, Quanying Liu:
Integration of cognitive tasks into artificial general intelligence test for large models. CoRR abs/2402.02547 (2024) - [i62]Binghui Xie, Yatao Bian, Kaiwen Zhou, Yongqiang Chen, Peilin Zhao, Bo Han, Wei Meng, James Cheng:
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations. CoRR abs/2402.03139 (2024) - [i61]Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao:
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping. CoRR abs/2402.07610 (2024) - [i60]Haiquan Qiu, Yatao Bian, Quanming Yao:
Graph Unitary Message Passing. CoRR abs/2403.11199 (2024) - [i59]Yongqiang Chen, Yatao Bian, Bo Han, James Cheng:
How Interpretable Are Interpretable Graph Neural Networks? CoRR abs/2406.07955 (2024) - [i58]Yuchang Zhu, Jintang Li, Yatao Bian, Zibin Zheng, Liang Chen:
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. CoRR abs/2406.13544 (2024) - [i57]Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian:
HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment. CoRR abs/2406.14021 (2024) - [i56]Juzheng Zhang, Yatao Bian, Yongqiang Chen, Quanming Yao:
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation. CoRR abs/2408.00863 (2024) - [i55]Haoyu Wang, Bingzhe Wu, Yatao Bian, Yongzhe Chang, Xueqian Wang, Peilin Zhao:
Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation. CoRR abs/2408.10668 (2024) - [i54]Shiguang Wu, Yaqing Wang, Yatao Bian, Quanming Yao:
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning. CoRR abs/2410.05975 (2024) - [i53]Qingyang Zhang, Yatao Bian, Xinke Kong, Peilin Zhao, Changqing Zhang:
COME: Test-time adaption by Conservatively Minimizing Entropy. CoRR abs/2410.10894 (2024) - [i52]Qingyang Zhang, Qiuxuan Feng, Joey Tianyi Zhou, Yatao Bian, Qinghua Hu, Changqing Zhang:
The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection. CoRR abs/2410.11576 (2024) - 2023
- [c41]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:
DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery - a Focus on Affinity Prediction Problems with Noise Annotations. AAAI 2023: 8023-8031 - [c40]Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
SAILOR: Structural Augmentation Based Tail Node Representation Learning. CIKM 2023: 1389-1399 - [c39]Liang Chen, Yang Deng, Yatao Bian, Zeyu Qin, Bingzhe Wu, Tat-Seng Chua, Kam-Fai Wong:
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators. EMNLP 2023: 6325-6341 - [c38]Haotian Wang, Zhen Zhang, Mengting Hu, Qichao Wang, Liang Chen, Yatao Bian, Bingzhe Wu:
RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data. EMNLP (Findings) 2023: 7246-7257 - [c37]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng:
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. ICLR 2023 - [c36]Fu-Yun Wang, Da-Wei Zhou, Liu Liu, Han-Jia Ye, Yatao Bian, De-Chuan Zhan, Peilin Zhao:
BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion. ICLR 2023 - [c35]Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng:
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? NeurIPS 2023 - [c34]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Understanding and Improving Feature Learning for Out-of-Distribution Generalization. NeurIPS 2023 - [c33]Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, Bingzhe Wu:
Fairness-guided Few-shot Prompting for Large Language Models. NeurIPS 2023 - [c32]Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan:
Simplifying and Empowering Transformers for Large-Graph Representations. NeurIPS 2023 - [c31]Xiang Zhuang, Qiang Zhang, Keyan Ding, Yatao Bian, Xiao Wang, Jingsong Lv, Hongyang Chen, Huajun Chen:
Learning Invariant Molecular Representation in Latent Discrete Space. NeurIPS 2023 - [c30]Hanwen Liu, Peilin Zhao, Tingyang Xu, Yatao Bian, Junzhou Huang, Yuesheng Zhu, Yadong Mu:
Curriculum Graph Poisoning. WWW 2023: 2011-2021 - [i51]Ziqiao Zhang, Bangyi Zhao, Ailin Xie, Yatao Bian, Shuigeng Zhou:
Activity Cliff Prediction: Dataset and Benchmark. CoRR abs/2302.07541 (2023) - [i50]Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, Bingzhe Wu:
Fairness-guided Few-shot Prompting for Large Language Models. CoRR abs/2303.13217 (2023) - [i49]Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Qinghua Hu, Bingzhe Wu, Changqing Zhang, Jianhua Yao:
Reweighted Mixup for Subpopulation Shift. CoRR abs/2304.04148 (2023) - [i48]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng:
Towards Understanding Feature Learning in Out-of-Distribution Generalization. CoRR abs/2304.11327 (2023) - [i47]Yuanfeng Ji, Yatao Bian, Guoji Fu, Peilin Zhao, Ping Luo:
SyNDock: N Rigid Protein Docking via Learnable Group Synchronization. CoRR abs/2305.15156 (2023) - [i46]Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan:
Simplifying and Empowering Transformers for Large-Graph Representations. CoRR abs/2306.10759 (2023) - [i45]Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
SAILOR: Structural Augmentation Based Tail Node Representation Learning. CoRR abs/2308.06801 (2023) - [i44]Liang Chen, Yang Deng, Yatao Bian, Zeyu Qin, Bingzhe Wu, Tat-Seng Chua, Kam-Fai Wong:
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators. CoRR abs/2310.07289 (2023) - [i43]Yiqiang Yi, Xu Wan, Yatao Bian, Le Ou-Yang, Peilin Zhao:
ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking. CoRR abs/2310.08061 (2023) - [i42]Xiang Zhuang, Qiang Zhang, Keyan Ding, Yatao Bian, Xiao Wang, Jingsong Lv, Hongyang Chen, Huajun Chen:
Learning Invariant Molecular Representation in Latent Discrete Space. CoRR abs/2310.14170 (2023) - [i41]Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng:
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? CoRR abs/2310.19035 (2023) - [i40]Liang Chen, Yatao Bian, Yang Deng, Shuaiyi Li, Bingzhe Wu, Peilin Zhao, Kam-Fai Wong:
X-Mark: Towards Lossless Watermarking Through Lexical Redundancy. CoRR abs/2311.09832 (2023) - [i39]Yongqiang Chen, Binghui Xie, Kaiwen Zhou, Bo Han, Yatao Bian, James Cheng:
Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes. CoRR abs/2311.18194 (2023) - [i38]Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, Zibin Zheng:
Rethinking and Simplifying Bootstrapped Graph Latents. CoRR abs/2312.02619 (2023) - 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) - [c29]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 - [c28]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 - [c27]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 - [c26]Guoji Fu, Peilin Zhao, Yatao Bian:
p-Laplacian Based Graph Neural Networks. ICML 2022: 6878-6917 - [c25]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 - [c24]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 - [c23]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 - [c22]Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS 2022 - [c21]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. NeurIPS 2022 - [c20]Zijing Ou, Tingyang Xu, Qinliang Su, Yingzhen Li, Peilin Zhao, Yatao Bian:
Learning Neural Set Functions Under the Optimal Subset Oracle. NeurIPS 2022 - [c19]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 - [c18]Yuzhao Chen, Yatao Bian, Jiying Zhang, Xi Xiao, Tingyang Xu, Yu Rong:
Diversified Multiscale Graph Learning with Graph Self-Correction. TAG-ML 2022: 48-54 - [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]