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Yatao 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
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
Recognizing Predictive Substructures With Subgraph Information Bottleneck. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1650-1663 (2024) - [j3]Yifan He
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
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. KDD 2024: 4688-4699 - [c42]Wangbin Sun
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
Integration of cognitive tasks into artificial general intelligence test for large models. CoRR abs/2402.02547 (2024) - [i62]Binghui Xie, Yatao Bian
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
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping. CoRR abs/2402.07610 (2024) - [i60]Haiquan Qiu, Yatao Bian
Graph Unitary Message Passing. CoRR abs/2403.11199 (2024) - [i59]Yongqiang Chen, Yatao Bian
How Interpretable Are Interpretable Graph Neural Networks? CoRR abs/2406.07955 (2024) - [i58]Yuchang Zhu, Jintang Li, Yatao Bian
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
SAILOR: Structural Augmentation Based Tail Node Representation Learning. CIKM 2023: 1389-1399 - [c39]Liang Chen, Yang Deng
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
Curriculum Graph Poisoning. WWW 2023: 2011-2021 - [i51]Ziqiao Zhang, Bangyi Zhao, Ailin Xie, Yatao Bian
Activity Cliff Prediction: Dataset and Benchmark. CoRR abs/2302.07541 (2023) - [i50]Huan Ma, Changqing Zhang, Yatao Bian
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
Reweighted Mixup for Subpopulation Shift. CoRR abs/2304.04148 (2023) - [i48]Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian
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
Simplifying and Empowering Transformers for Large-Graph Representations. CoRR abs/2306.10759 (2023) - [i45]Jie Liao, Jintang Li, Liang Chen, Bingzhe Wu
SAILOR: Structural Augmentation Based Tail Node Representation Learning. CoRR abs/2308.06801 (2023) - [i44]Liang Chen, Yang Deng
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators. CoRR abs/2310.07289 (2023) - [i43]Yiqiang Yi, Xu Wan, Yatao Bian
ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking. CoRR abs/2310.08061 (2023) - [i42]Xiang Zhuang, Qiang Zhang, Keyan Ding, Yatao Bian
Learning Invariant Molecular Representation in Latent Discrete Space. CoRR abs/2310.14170 (2023) - [i41]Yongqiang Chen, Yatao Bian
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? CoRR abs/2310.19035 (2023) - [i40]Liang Chen, Yatao Bian
X-Mark: Towards Lossless Watermarking Through Lexical Redundancy. CoRR abs/2311.09832 (2023) - [i39]Yongqiang Chen, Binghui Xie, Kaiwen Zhou, Bo Han, Yatao Bian
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
Rethinking and Simplifying Bootstrapped Graph Latents. CoRR abs/2312.02619 (2023) - 2022
- [j2]Hehuan Ma
Cross-dependent graph neural networks for molecular property prediction. Bioinform. 38(7): 2003-2009 (2022) - [c29]Weizhi An, Yuzhi Guo, Yatao Bian
MoDNA: motif-oriented pre-training for DNA language model. BCB 2022: 5:1-5:5 - [c28]Yatao Bian
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
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
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
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
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
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs. CoRR abs/2203.16939 (2022) - [i30]Bingzhe Wu
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup. CoRR abs/2204.07742 (2022) - [i29]Bingzhe Wu
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR abs/2205.10014 (2022) - [i28]Yongqiang Chen
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
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
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
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
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
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
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
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
Greedy MaxCut algorithms and their information content. ITW 2015: 1-5 - 2013
- [c2]Yatao Bian
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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