Travelled to:1 × Germany
1 × Israel
3 × USA
4 × Canada
Collaborated with:J.D.Lafferty N.E.Rosenblum B.P.Miller D.Andrzejewski M.Craven Y.Liu Z.Ghahramani G.Druck C.Pal A.McCallum B.R.Gibson K.Jun T.T.Rogers J.Harrison C.Kalish C.Gokhale S.Das A.Doan J.F.Naughton N.Rampalli J.W.Shavlik
Talks about:supervis (3) model (3) learn (3) semi (3) proven (2) mixtur (2) method (2) harmon (2) binari (2) field (2)
Person: Xiaojin Zhu
DBLP: Zhu:Xiaojin
Contributed to:
2014
2011
2010
20102009
2007200520042003Wrote 9 papers:
- SIGMOD-2014-GokhaleDDNRSZ #crowdsourcing #named
- Corleone: hands-off crowdsourcing for entity matching (CG, SD, AD, JFN, NR, JWS, XZ), pp. 601–612.
- ISSTA-2011-RosenblumMZ
- Recovering the toolchain provenance of binary code (NER, BPM, XZ), pp. 100–110.
- ICML-2010-ZhuGJRHK #learning #modelling
- Cognitive Models of Test-Item Effects in Human Category Learning (XZ, BRG, KSJ, TTR, JH, CK), pp. 1247–1254.
- PASTE-2010-RosenblumMZ #compilation
- Extracting compiler provenance from program binaries (NER, BPM, XZ), pp. 21–28.
- ICML-2009-AndrzejewskiZC #modelling #topic
- Incorporating domain knowledge into topic modeling via Dirichlet Forest priors (DA, XZ, MC), pp. 25–32.
- KDD-2007-DruckPMZ #classification #generative #hybrid
- Semi-supervised classification with hybrid generative/discriminative methods (GD, CP, AM, XZ), pp. 280–289.
- ICML-2005-ZhuL #graph #induction #learning #modelling #scalability
- Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning (XZ, JDL), pp. 1052–1059.
- ICML-2004-LaffertyZL #clique #kernel #random #representation
- Kernel conditional random fields: representation and clique selection (JDL, XZ, YL).
- ICML-2003-ZhuGL #learning #using
- Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions (XZ, ZG, JDL), pp. 912–919.