Travelled to:1 × Australia
1 × China
1 × United Kingdom
5 × USA
Collaborated with:R.D.Lawrence H.Yang D.Zhang Y.Liu P.Yang R.Lawrence L.Si Y.Zhu M.Li H.Zhang C.Zhang H.Tong Z.Wen R.Konuru C.Lin
Talks about:learn (6) multi (5) multipl (3) heterogen (2) transfer (2) instanc (2) optim (2) model (2) graph (2) view (2)
Person: Jingrui He
DBLP: He:Jingrui
Facilitated 1 volumes:
EdContributed to:
20152014
2013201320112011
2009
2004Wrote 10 papers:
- KDD-2015-YangH #learning #multi
- Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning (PY, JH), pp. 1375–1384.
- KDD-2015-ZhuYH #clustering #optimisation #predict
- Co-Clustering based Dual Prediction for Cargo Pricing Optimization (YZ, HY, JH), pp. 1583–1592.
- KDD-2014-YangH #learning #parametricity
- Learning with dual heterogeneity: a nonparametric bayes model (HY, JH), pp. 582–590.
- ICML-c3-2013-ZhangHSL #learning #multi #named
- MILEAGE: Multiple Instance LEArning with Global Embedding (DZ, JH, LS, RDL), pp. 82–90.
- KDD-2013-ZhangHL #learning #multi #named
- MI2LS: multi-instance learning from multiple informationsources (DZ, JH, RDL), pp. 149–157.
- ICML-2011-HeL #framework #learning #multi
- A Graphbased Framework for Multi-Task Multi-View Learning (JH, RL), pp. 25–32.
- KDD-2011-TongHWKL #graph #optimisation #ranking #scalability
- Diversified ranking on large graphs: an optimization viewpoint (HT, JH, ZW, RK, CYL), pp. 1028–1036.
- KDD-2011-ZhangHLSL #approach #learning #multi #scalability
- Multi-view transfer learning with a large margin approach (DZ, JH, YL, LS, RDL), pp. 1208–1216.
- CIKM-2009-HeLL #graph #learning
- Graph-based transfer learning (JH, YL, RDL), pp. 937–946.
- ICPR-v1-2004-HeLZZ #classification #image #web
- W-Boost and Its Application to Web Image Classification (JH, ML, HZ, CZ), pp. 148–151.