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- Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
- ICML
- 2003

An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weightsâ€¦ (More)

- Xiaojin Zhu, Andrew B. Goldberg
- Introduction to Semi-Supervised Learning
- 2009

- David Andrzejewski, Xiaojin Zhu, Mark Craven
- ICML
- 2009

Users of topic modeling methods often have knowledge about the composition of words that should have high or low probability in various topics. We incorporate such domain knowledge using a novelâ€¦ (More)

We present an algorithm based on convex optimization for constructing kernels for semi-supervised learning. The kernel matrices are derived from the spectral decomposition of graph Laplacians, andâ€¦ (More)

We pose transductive classification as a matrix completion problem. By assuming the underlying matrix has a low rank, our formulation is able to handle three problems simultaneously: i) multi-labelâ€¦ (More)

- Xiaojin Zhu, Andrew B. Goldberg, Jurgen Van Gael, David Andrzejewski
- HLT-NAACL
- 2007

We introduce a novel ranking algorithm called GRASSHOPPER, which ranks items with an emphasis on diversity. That is, the top items should be different from each other in order to have a broadâ€¦ (More)

- John D. Lafferty, Xiaojin Zhu, Yan Liu
- ICML
- 2004

Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given whichâ€¦ (More)

- Chaitanya Gokhale, Sanjib Das, +4 authors Xiaojin Zhu
- SIGMOD Conference
- 2014

Recent approaches to crowdsourcing entity matching (EM) are limited in that they crowdsource only parts of the EM workflow, requiring a developer to execute the remaining parts. Consequently, theseâ€¦ (More)

- Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu
- EMNLP-CoNLL
- 2007

We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inferenceâ€¦ (More)

In this paper we propose algorithms to automatically classify sentences into metaphoric or normal usages. Our algorithms only need the WordNet and bigram counts, and does not require training. Weâ€¦ (More)