Travelled to:1 × Finland
1 × France
1 × Germany
4 × USA
Collaborated with:D.Schuurmans T.Xia C.Lee R.Greiner F.Jiao L.Cheng S.Zhai M.Tan L.Guo L.Zheng Y.Liu F.Peng Y.Zhao G.Haffari Y.Wang G.Mori S.Wang
Talks about:model (4) direct (3) boost (3) bayesian (2) regular (2) inform (2) optim (2) learn (2) rank (2) incomplet (1)
Person: Shaojun Wang
DBLP: Wang:Shaojun
Contributed to:
201420132009
2008200620052003Wrote 8 papers:
- KDD-2014-ZhaiXW #multi #optimisation
- A multi-class boosting method with direct optimization (SZ, TX, SW), pp. 273–282.
- KDD-2013-TanXGW #learning #metric #modelling #optimisation #rank #ranking
- Direct optimization of ranking measures for learning to rank models (MT, TX, LG, SW), pp. 856–864.
- KDD-2009-ZhengWLL
- Information theoretic regularization for semi-supervised boosting (LZ, SW, YL, CHL), pp. 1017–1026.
- ICML-2008-HaffariWWMJ
- Boosting with incomplete information (GH, YW, SW, GM, FJ), pp. 368–375.
- ICML-2006-LeeGW #classification #using
- Using query-specific variance estimates to combine Bayesian classifiers (CHL, RG, SW), pp. 529–536.
- ICML-2005-ChengJSW #image #modelling
- Variational Bayesian image modelling (LC, FJ, DS, SW), pp. 129–136.
- ICML-2005-WangWGSC #markov #modelling #random #semantics
- Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields (SW, SW, RG, DS, LC), pp. 948–955.
- ICML-2003-WangSPZ #learning #modelling #principle
- Learning Mixture Models with the Latent Maximum Entropy Principle (SW, DS, FP, YZ), pp. 784–791.