Travelled to:1 × Australia
1 × Finland
1 × Japan
1 × Spain
1 × Sweden
1 × The Netherlands
1 × Turkey
2 × China
7 × USA
Collaborated with:J.Hu C.Zhang P.Cui W.Zhu S.Yang X.Wang J.Zhou J.Sun B.Zhao F.Shang L.C.Jiao T.Li M.Ou S.Chen T.Rompf M.Jiang T.F.Syeda-Mahmood D.Beymer Q.Yang J.Ye J.Wang Z.Sun D.Sontag T.Wang D.Wang D.Zhang L.Si C.Liu H.Xiong Y.Liu Y.Song P.Zhang B.Qian I.Davidson N.Lee S.Ebadollahi X.Xu S.Jin L.Yu Z.Lu L.Yuan S.Liu L.Sun R.Liu A.Mahmood R.J.Lundstrom N.Shafee T.Holve M.Yuan K.Deng J.Zeng Y.Li B.Ni X.He W.Dai F.Lin K.Z.Y.Ang B.M.Chen T.H.Lee B.Yang M.Dong X.Dong J.Cui S.K.Phang B.Wang D.Luo S.Zhao M.Yin K.Li K.Peng G.Cai
Talks about:predict (5) learn (5) data (5) supervis (4) approach (4) semi (4) discoveri (3) electron (3) social (3) record (3)
Person: Fei Wang
DBLP: Wang:Fei
Contributed to:
2015
20142014
2014
20132013
201220122012
20112010
20092008
2008200820072006
2017Wrote 32 papers:
- KDD-2015-LiuWHX #framework #graph #health
- Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework (CL, FW, JH, HX), pp. 705–714.
- KDD-2015-OuCWW0 #component #similarity #transitive
- Non-transitive Hashing with Latent Similarity Components (MO, PC, FW, JW, WZ), pp. 895–904.
- KDD-2015-SunWH #approach #named #predict #risk management
- LINKAGE: An Approach for Comprehensive Risk Prediction for Care Management (ZS, FW, JH), pp. 1145–1154.
- ICPR-2014-WangWH #framework #learning #multi #predict #risk management
- A Multi-task Learning Framework for Joint Disease Risk Prediction and Comorbidity Discovery (XW, FW, JH), pp. 220–225.
- ICPR-2014-WangZH #approach #data-driven #health #named
- DensityTransfer: A Data Driven Approach for Imputing Electronic Health Records (FW, JZ, JH), pp. 2763–2768.
- KDD-2014-JiangCWXZY #analysis #behaviour #flexibility #multi #named
- FEMA: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery (MJ, PC, FW, XX, WZ, SY), pp. 1186–1195.
- KDD-2014-WangSW #learning #modelling
- Unsupervised learning of disease progression models (XW, DS, FW), pp. 85–94.
- KDD-2014-WangWW
- Quantifying herding effects in crowd wisdom (TW, DW, FW), pp. 1087–1096.
- KDD-2014-WangZQWD #multi #predict #risk management
- Clinical risk prediction with multilinear sparse logistic regression (FW, PZ, BQ, XW, ID), pp. 145–154.
- KDD-2014-ZhouWHY #data-driven #metaprogramming
- From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records (JZ, FW, JH, JY), pp. 135–144.
- VLDB-2014-YuanDZLNHWDY #distributed #mobile #named #scalability
- OceanST: A Distributed Analytic System for Large-Scale Spatiotemporal Mobile Broadband Data (MY, KD, JZ, YL, BN, XH, FW, WD, QY), pp. 1561–1564.
- DUXU-NTE-2013-LinAWCLYDDCPWLZYLPC #development
- Development of an Unconventional Unmanned Coaxial Rotorcraft: GremLion (FL, KZYA, FW, BMC, THL, BY, MD, XD, JC, SKP, BW, DL, SZ, MY, KL, KP, GC), pp. 120–129.
- KDD-2013-CuiJYWZY #approach #data-driven #network #predict
- Cascading outbreak prediction in networks: a data-driven approach (PC, SJ, LY, FW, WZ, SY), pp. 901–909.
- KDD-2013-OuCWWZY #scalability
- Comparing apples to oranges: a scalable solution with heterogeneous hashing (MO, PC, FW, JW, WZ, SY), pp. 230–238.
- KDD-2013-ZhouLSYWY #identification #named
- FeaFiner: biomarker identification from medical data through feature generalization and selection (JZ, ZL, JS, LY, FW, JY), pp. 1034–1042.
- CIKM-2012-JiangCLYWZY #recommendation #social
- Social contextual recommendation (MJ, PC, RL, QY, FW, WZ, SY), pp. 45–54.
- CIKM-2012-JiangCWYZY #multi #recommendation #relational #social
- Social recommendation across multiple relational domains (MJ, PC, FW, QY, WZ, SY), pp. 1422–1431.
- CIKM-2012-ShangJLW #learning
- Learning spectral embedding via iterative eigenvalue thresholding (FS, LCJ, YL, FW), pp. 1507–1511.
- ICPR-2012-0001HS #feedback #similarity
- Medical prognosis based on patient similarity and expert feedback (FW, JH, JS), pp. 1799–1802.
- KDD-2012-0001LHSE #approach #towards
- Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach (FW, NL, JH, JS, SE), pp. 453–461.
- KDD-2012-ShangJW #learning
- Semi-supervised learning with mixed knowledge information (FS, LCJ, FW), pp. 732–740.
- SIGIR-2011-CuiWLOYS #predict #ranking #social #what
- Who should share what?: item-level social influence prediction for users and posts ranking (PC, FW, SL, MO, SY, LS), pp. 185–194.
- SIGIR-2011-ZhangWS #multi
- Composite hashing with multiple information sources (DZ, FW, LS), pp. 225–234.
- ICPR-2010-Syeda-MahmoodBWMLSH #automation
- Automatic Selection of Keyframes from Angiogram Videos (TFSM, DB, FW, AM, RJL, NS, TH), pp. 4008–4011.
- ICDAR-2009-WangSB #documentation #information management #multimodal
- Information Extraction from Multimodal ECG Documents (FW, TFSM, DB), pp. 381–385.
- CIKM-2008-ChenWSZ #ranking
- Semi-supervised ranking aggregation (SC, FW, YS, CZ), pp. 1427–1428.
- CIKM-2008-WangCZL #constraints #learning #metric
- Semi-supervised metric learning by maximizing constraint margin (FW, SC, CZ, TL), pp. 1457–1458.
- ICML-2008-ZhaoWZ #clustering #multi #performance
- Efficient multiclass maximum margin clustering (BZ, FW, CZ), pp. 1248–1255.
- KDD-2008-ZhaoWZ #algorithm #named #performance #virtual machine
- Cuts3vm: a fast semi-supervised svm algorithm (BZ, FW, CZ), pp. 830–838.
- SIGIR-2007-WangZL #clustering #documentation
- Regularized clustering for documents (FW, CZ, TL), pp. 95–102.
- ICML-2006-WangZ #linear
- Label propagation through linear neighborhoods (FW, CZ), pp. 985–992.
- ECOOP-2017-WangR #normalisation #towards
- Towards Strong Normalization for Dependent Object Types (DOT) (FW, TR), p. 25.