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ORCIDJiang Bian 0002
Person information
- affiliation: Microsoft Research Asia, Beijing, China
- affiliation (former): Yahoo! Labs, Sunnyvale, CA, USA
- affiliation (PhD 2010): Georgia Institute of Technology, Atlanta, GA, USA
Other persons with the same name
- Jiang Bian — disambiguation page
- Jiang Bian 0001
![[0000-0002-2238-5429]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-2238-5429")
— University of Florida, Department of Health Outcomes and Biomedical Informatics, Gainesville, FL, USA (and 1 more) - Jiang Bian 0003 — Baidu Research, Big Data Laboratory, Beijing, China (and 1 more)
- Jiang Bian 0004 — Chinese Academy of Sciences, Institute of Automation, State Key Laboratory for Management and Control of Complex Systems, Beijing, China
- Jiang Bian 0005 — Northwest A&F University, Yangling, China
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2020 – today
- 2022
[c56]Min Hou, Chang Xu, Zhi Li, Yang Liu, Weiqing Liu, Enhong Chen, Jiang Bian:
Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction. WWW 2022: 112-121- [i43]Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian:
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation. CoRR abs/2201.04038 (2022) - [i42]Di He, Wenlei Shi, Shanda Li, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu:
Learning Physics-Informed Neural Networks without Stacked Back-propagation. CoRR abs/2202.09340 (2022) - [i41]Lin Huang, Qiyuan Dong, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu:
AF2: Adaptive Focus Framework for Aerial Imagery Segmentation. CoRR abs/2202.10322 (2022) - [i40]Lin Huang, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu:
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting. CoRR abs/2202.10586 (2022) - [i39]Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu:
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting. CoRR abs/2203.07681 (2022) - [i38]Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li:
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble. CoRR abs/2205.09284 (2022) - [i37]Wenlei Shi, Xinquan Huang, Xiaotian Gao, Xinran Wei, Jia Zhang, Jiang Bian, Mao Yang, Tie-Yan Liu:
LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data. CoRR abs/2206.09418 (2022) - 2021
- [j9]Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu:
Demonstration actor critic. Neurocomputing 434: 194-202 (2021) - [j8]Xia Hu, Lingyang Chu, Jian Pei, Weiqing Liu, Jiang Bian:
Model complexity of deep learning: a survey. Knowl. Inf. Syst. 63(10): 2585-2619 (2021) - [c55]Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu:
Universal Trading for Order Execution with Oracle Policy Distillation. AAAI 2021: 107-115 - [c54]Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Xiang-Yang Li:
Learning to Reweight with Deep Interactions. AAAI 2021: 7385-7393 - [c53]Shun Zheng, Wei Cao, Wei Xu, Jiang Bian:
Revisiting the Evaluation of End-to-end Event Extraction. ACL/IJCNLP (Findings) 2021: 4609-4617 - [c52]Wenlei Shi, Xinran Wei, Jia Zhang, Xiaoyuan Ni, Arthur Jiang, Jiang Bian, Tie-Yan Liu:
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations. AAMAS 2021: 1191-1199 - [c51]Min Hou, Chang Xu, Yang Liu, Weiqing Liu, Jiang Bian, Le Wu, Zhi Li, Enhong Chen, Tie-Yan Liu:
Stock Trend Prediction with Multi-granularity Data: A Contrastive Learning Approach with Adaptive Fusion. CIKM 2021: 700-709 - [c50]Shun Zheng, Zhifeng Gao, Wei Cao, Jiang Bian, Tie-Yan Liu:
HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting. CIKM 2021: 4383-4392 - [c49]Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian:
Deep risk model: a deep learning solution for mining latent risk factors to improve covariance matrix estimation. ICAIF 2021: 12:1-12:8 - [c48]Tianhao Zhang, Qiwei Ye, Jiang Bian, Guangming Xie, Tie-Yan Liu:
MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks. IJCAI 2021: 500-506 - [c47]Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minlie Huang, Tao Qin, Tie-Yan Liu:
Independence-aware Advantage Estimation. IJCAI 2021: 3349-3355 - [c46]Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian:
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport. KDD 2021: 1017-1026 - [c45]Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu:
REST: Relational Event-driven Stock Trend Forecasting. WWW 2021: 1-10 - [i36]Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu:
REST: Relational Event-driven Stock Trend Forecasting. CoRR abs/2102.07372 (2021) - [i35]Xia Hu, Lingyang Chu, Jian Pei, Weiqing Liu, Jiang Bian:
Model Complexity of Deep Learning: A Survey. CoRR abs/2103.05127 (2021) - [i34]Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu:
Universal Trading for Order Execution with Oracle Policy Distillation. CoRR abs/2103.10860 (2021) - [i33]Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian:
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport. CoRR abs/2106.12950 (2021) - [i32]Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian:
Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation. CoRR abs/2107.05201 (2021) - [i31]Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu:
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting. CoRR abs/2109.06489 (2021) - [i30]Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, Tie-Yan Liu:
HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information. CoRR abs/2110.13716 (2021) - [i29]Zhining Liu, Zhepei Wei, Erxin Yu, Qiang Huang, Kai Guo, Boyang Yu, Zhaonian Cai, Hangting Ye, Wei Cao, Jiang Bian, Pengfei Wei, Jing Jiang, Yi Chang:
IMBENS: Ensemble Class-imbalanced Learning in Python. CoRR abs/2111.12776 (2021) - [i28]Zhining Liu, Pengfei Wei, Zhepei Wei, Boyang Yu, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang:
Towards Inter-class and Intra-class Imbalance in Class-imbalanced Learning. CoRR abs/2111.12791 (2021) - [i27]Wentao Xu, Zhiping Luo, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu:
KGE-CL: Contrastive Learning of Knowledge Graph Embeddings. CoRR abs/2112.04871 (2021) - [i26]Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu:
SHGNN: Structure-Aware Heterogeneous Graph Neural Network. CoRR abs/2112.06244 (2021) - 2020
- [c44]Zhenhui Xu, Guolin Ke, Jia Zhang, Jiang Bian, Tie-Yan Liu:
Light Multi-Segment Activation for Model Compression. AAAI 2020: 6542-6549 - [c43]Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu:
Invertible Image Rescaling. ECCV (1) 2020: 126-144 - [c42]Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu:
Self-paced Ensemble for Highly Imbalanced Massive Data Classification. ICDE 2020: 841-852 - [c41]Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei:
Measuring Model Complexity of Neural Networks with Curve Activation Functions. KDD 2020: 1521-1531 - [c40]Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang:
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler. NeurIPS 2020 - [i25]Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu:
Invertible Image Rescaling. CoRR abs/2005.05650 (2020) - [i24]Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Liwei Wang, Jiang Bian, Tie-Yan Liu:
MC-BERT: Efficient Language Pre-Training via a Meta Controller. CoRR abs/2006.05744 (2020) - [i23]Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei:
Measuring Model Complexity of Neural Networks with Curve Activation Functions. CoRR abs/2006.08962 (2020) - [i22]Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Xiang-Yang Li, Tie-Yan Liu:
Learning to Teach with Deep Interactions. CoRR abs/2007.04649 (2020) - [i21]Xueqing Wu, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Tie-Yan Liu:
Learn to Use Future Information in Simultaneous Translation. CoRR abs/2007.05290 (2020) - [i20]Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, Tie-Yan Liu:
Qlib: An AI-oriented Quantitative Investment Platform. CoRR abs/2009.11189 (2020) - [i19]Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang:
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler. CoRR abs/2010.08830 (2020) - [i18]Hao Wang, Jia Zhang, Yingce Xia, Jiang Bian, Chao Zhang, Tie-Yan Liu:
COSEA: Convolutional Code Search with Layer-wise Attention. CoRR abs/2010.09520 (2020) - [i17]Hongshun Tang, Lijun Wu, Weiqing Liu, Jiang Bian:
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting. CoRR abs/2012.06289 (2020) - [i16]Wenlei Shi, Xinran Wei, Jia Zhang, Xiaoyuan Ni, Arthur Jiang, Jiang Bian, Tie-Yan Liu:
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations. CoRR abs/2012.13099 (2020)
2010 – 2019
- 2019
- [j7]Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Enhong Chen, Tie-Yan Liu:
Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation. IEEE ACM Trans. Audio Speech Lang. Process. 27(10): 1564-1576 (2019) - [c39]Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu:
Trust Region Evolution Strategies. AAAI 2019: 4352-4359 - [c38]Zichuan Lin, Li Zhao, Jiang Bian, Tao Qin, Guangwen Yang:
Unified Policy Optimization for Robust Reinforcement Learning. ACML 2019: 395-410 - [c37]Xihan Li, Jia Zhang, Jiang Bian, Yunhai Tong, Tie-Yan Liu:
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network. AAMAS 2019: 980-988 - [c36]Shun Zheng, Wei Cao, Wei Xu, Jiang Bian:
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction. EMNLP/IJCNLP (1) 2019: 337-346 - [c35]Lewen Wang, Weiqing Liu, Xiao Yang, Jiang Bian:
Conservative or Aggressive? Confidence-Aware Dynamic Portfolio Construction. GlobalSIP 2019: 1-5 - [c34]Xiao Yang, Weiqing Liu, Lewen Wang, Cheng Qu, Jiang Bian:
A Divide-and-Conquer Framework for Attention-based Combination of Multiple Investment Strategies. GlobalSIP 2019: 1-5 - [c33]Guolin Ke, Zhenhui Xu, Jia Zhang, Jiang Bian, Tie-Yan Liu:
DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks. KDD 2019: 384-394 - [c32]Zhige Li, Derek Yang, Li Zhao, Jiang Bian, Tao Qin, Tie-Yan Liu:
Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding. KDD 2019: 894-902 - [c31]Chi Chen, Li Zhao, Jiang Bian, Chunxiao Xing, Tie-Yan Liu:
Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction. KDD 2019: 2376-2384 - [c30]Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu:
Fully Parameterized Quantile Function for Distributional Reinforcement Learning. NeurIPS 2019: 6190-6199 - [i15]Xihan Li, Jia Zhang, Jiang Bian, Yunhai Tong, Tie-Yan Liu:
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network. CoRR abs/1903.00714 (2019) - [i14]Shun Zheng, Wei Cao, Wei Xu, Jiang Bian:
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction. CoRR abs/1904.07535 (2019) - [i13]Zhenhui Xu, Guolin Ke, Jia Zhang, Jiang Bian, Tie-Yan Liu:
Light Multi-segment Activation for Model Compression. CoRR abs/1907.06870 (2019) - [i12]Ziyu Liu, Guolin Ke, Jiang Bian, Tie-Yan Liu:
LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm. CoRR abs/1908.09362 (2019) - [i11]Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu:
Self-paced Ensemble for Highly Imbalanced Massive Data Classification. CoRR abs/1909.03500 (2019) - [i10]Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu:
Fully Parameterized Quantile Function for Distributional Reinforcement Learning. CoRR abs/1911.02140 (2019) - 2018
- [c29]Yijun Wang, Yingce Xia, Li Zhao, Jiang Bian, Tao Qin, Guiquan Liu, Tie-Yan Liu:
Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization. AAAI 2018: 5553-5560 - [c28]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning. AAMAS 2018: 721-729 - [c27]Yi Ding, Weiqing Liu, Jiang Bian, Daoqiang Zhang, Tie-Yan Liu:
Investor-Imitator: A Framework for Trading Knowledge Extraction. KDD 2018: 1310-1319 - [c26]Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu:
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction. WSDM 2018: 261-269 - 2017
- [c25]Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu:
Dual Supervised Learning. ICML 2017: 3789-3798 - [c24]Yingce Xia, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu:
Dual Inference for Machine Learning. IJCAI 2017: 3112-3118 - [c23]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks. ECML/PKDD (1) 2017: 187-202 - [i9]Yang Fan, Fei Tian, Tao Qin, Jiang Bian, Tie-Yan Liu:
Learning What Data to Learn. CoRR abs/1702.08635 (2017) - [i8]Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu:
Dual Supervised Learning. CoRR abs/1707.00415 (2017) - [i7]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Slim-DP: A Light Communication Data Parallelism for DNN. CoRR abs/1709.09393 (2017) - 2016
- [c22]Huazheng Wang, Fei Tian, Bin Gao, Chengjieren Zhu, Jiang Bian, Tie-Yan Liu:
Solving Verbal Questions in IQ Test by Knowledge-Powered Word Embedding. EMNLP 2016: 541-550 - [i6]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks. CoRR abs/1606.00575 (2016) - 2015
- [j6]Bo Long, Jiang Bian, Olivier Chapelle, Ya Zhang, Yoshiyuki Inagaki, Yi Chang:
Active Learning for Ranking through Expected Loss Optimization. IEEE Trans. Knowl. Data Eng. 27(5): 1180-1191 (2015) - [j5]Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Hanjun Dai, Tie-Yan Liu:
KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge. ACM Trans. Inf. Syst. 34(1): 4:1-4:25 (2015) - [i5]Huazheng Wang, Bin Gao, Jiang Bian, Fei Tian, Tie-Yan Liu:
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding. CoRR abs/1505.07909 (2015) - 2014
- [j4]Jiang Bian, Bo Long, Lihong Li, Taesup Moon, Anlei Dong, Yi Chang:
Exploiting User Preference for Online Learning in Web Content Optimization Systems. ACM Trans. Intell. Syst. Technol. 5(2): 33:1-33:23 (2014) - [c21]Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu:
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. AAAI 2014: 1369-1375 - [c20]Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, Gang Wang, Xiaoguang Liu, Tie-Yan Liu:
RC-NET: A General Framework for Incorporating Knowledge into Word Representations. CIKM 2014: 1219-1228 - [c19]Siyu Qiu, Qing Cui, Jiang Bian, Bin Gao, Tie-Yan Liu:
Co-learning of Word Representations and Morpheme Representations. COLING 2014: 141-150 - [c18]Fei Tian, Hanjun Dai, Jiang Bian, Bin Gao, Rui Zhang, Enhong Chen, Tie-Yan Liu:
A Probabilistic Model for Learning Multi-Prototype Word Embeddings. COLING 2014: 151-160 - [c17]Jiang Bian, Bin Gao, Tie-Yan Liu:
Knowledge-Powered Deep Learning for Word Embedding. ECML/PKDD (1) 2014: 132-148 - [c16]Jun Feng, Jiang Bian, Taifeng Wang, Wei Chen, Xiaoyan Zhu, Tie-Yan Liu:
Sampling dilemma: towards effective data sampling for click prediction in sponsored search. WSDM 2014: 103-112 - [i4]Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu:
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. CoRR abs/1404.5772 (2014) - [i3]Bin Gao, Jiang Bian, Tie-Yan Liu:
WordRep: A Benchmark for Research on Learning Word Representations. CoRR abs/1407.1640 (2014) - [i2]Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Tie-Yan Liu:
Learning Effective Word Embedding using Morphological Word Similarity. CoRR abs/1407.1687 (2014) - 2013
- [j3]Jiang Bian, Anlei Dong, Xiaofeng He, Srihari Reddy, Yi Chang:
User Action Interpretation for Online Content Optimization. IEEE Trans. Knowl. Data Eng. 25(9): 2161-2174 (2013) - [c15]Taifeng Wang, Jiang Bian, Shusen Liu, Yuyu Zhang, Tie-Yan Liu:
Psychological advertising: exploring user psychology for click prediction in sponsored search. KDD 2013: 563-571 - [c14]Yoshiyuki Inagaki, Jiang Bian, Yi Chang:
An effective general framework for localized content optimization. WWW (Companion Volume) 2013: 65-66 - 2012
- [j2]Jiang Bian, Yi Chang, Yun Fu, Wen-Yen Chen:
Learning to blend vitality rankings from heterogeneous social networks. Neurocomputing 97: 390-397 (2012) - [c13]Bo Long, Jiang Bian, Anlei Dong, Yi Chang:
Enhancing product search by best-selling prediction in e-commerce. CIKM 2012: 2479-2482 - [c12]Xuanhui Wang, Jiang Bian, Yi Chang, Belle L. Tseng:
Model news relatedness through user comments. WWW (Companion Volume) 2012: 629-630 - [i1]Shuang-Hong Yang, Jiang Bian, Hongyuan Zha:
Hybrid Generative/Discriminative Learning for Automatic Image Annotation. CoRR abs/1203.3530 (2012) - 2011
- [c11]Anlei Dong, Jiang Bian, Xiaofeng He, Srihari Reddy, Yi Chang:
User action interpretation for personalized content optimization in recommender systems. CIKM 2011: 2129-2132 - [c10]Jiang Bian, Yi Chang:
A taxonomy of local search: semi-supervised query classification driven by information needs. CIKM 2011: 2425-2428 - [c9]Yoshiyuki Inagaki, Jiang Bian, Yi Chang, Motoko Maki:
Enhancing mobile search using web search log data. SIGIR 2011: 1201-1202 - 2010
- [c8]Shuang-Hong Yang, Jiang Bian, Hongyuan Zha:
Hybrid Generative/Discriminative Learning for Automatic Image Annotation. UAI 2010: 683-690 - [c7]Jiang Bian, Tie-Yan Liu, Tao Qin, Hongyuan Zha:
Ranking with query-dependent loss for web search. WSDM 2010: 141-150 - [c6]Jiang Bian, Xin Li, Fan Li, Zhaohui Zheng, Hongyuan Zha:
Ranking specialization for web search: a divide-and-conquer approach by using topical RankSVM. WWW 2010: 131-140
2000 – 2009
- 2009
- [j1]Eugene Agichtein, Yandong Liu, Jiang Bian:
Modeling information-seeker satisfaction in community question answering. ACM Trans. Knowl. Discov. Data 3(2): 10:1-10:27 (2009) - [c5]Jiang Bian, Yandong Liu, Ding Zhou, Eugene Agichtein, Hongyuan Zha:
Learning to recognize reliable users and content in social media with coupled mutual reinforcement. WWW 2009: 51-60 - 2008
- [c4]Jiang Bian, Yandong Liu, Eugene Agichtein, Hongyuan Zha:
A few bad votes too many?: towards robust ranking in social media. AIRWeb 2008: 53-60 - [c3]Yandong Liu, Jiang Bian, Eugene Agichtein:
Predicting information seeker satisfaction in community question answering. SIGIR 2008: 483-490 - [c2]Jiang Bian, Yandong Liu, Eugene Agichtein, Hongyuan Zha:
Finding the right facts in the crowd: factoid question answering over social media. WWW 2008: 467-476 - [c1]Ding Zhou, Jiang Bian, Shuyi Zheng, Hongyuan Zha, C. Lee Giles:
Exploring social annotations for information retrieval. WWW 2008: 715-724
Coauthor Index
[i42] [i41] [i40] [i39] [i37] [j9] [c55] [c52] [c51] [c50] [c48] [c47] [c45] [i36] [i34] [i31] [i30] [i27] [i26] [c44] [c43] [c42] [i25] [i24] [i22] [i21] [i20] [i18] [i16] [j7] [c39] [c37] [c33] [c32] [c31] [c30] [i15] [i13] [i12] [i11] [i10] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [i9] [i8] [i7] [c22] [i6] [j5] [i5] [c21] [c20] [c19] [c18] [c17] [c16] [i4] [i3] [i2] [c15] [c7]
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