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Semi-supervised discriminative learning and structured prediction
- A rate distortion approach for semi-supervised conditional random fields
Y. Wang, G. Haffari, S. Wang and G. Mori
Advances in Neural Information Processing Systems, NIPS-2009. [pdf]
- Information theoretic regularization for semi-supervised boosting
L. Zheng, S. Wang, Y. Liu and C. Lee
The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2009.[pdf]
- Boosting with incomplete information
G. Haffari, Y. Wang, S. Wang, G. Mori and F. Jiao
The 25th International Conference on Machine Learning, ICML-2008. [pdf]
- Semi-supervised conditional random fields for improved
sequence segmentation and labeling
F. Jiao, S. Wang, C. Lee,
R. Greiner and D. Schuurmans
The Joint 21st International
Conference on Computational Linguistics and 44th Annual Meeting of the
Association for Computational Linguistics, COLING/ACL-2006. [pdf]
- Learning to model spatial dependency: Semi-supervised
discriminative random fields
C. Lee, S. Wang, F. Jiao, D.
Schuurmans and R. Greiner
Advances in Neural Information
Processing Systems, NIPS-2006. [pdf]
Statistical language modeling
- Exploiting syntactic, semantic and lexical regularities in
language modeling via directed Markov random fields
S.
Wang, S. Wang R. Greiner, D. Schuurmans and L. Cheng. [pdf]
shorter version appeared at The 22th International Conference on
Machine Learning, ICML-2005. [pdf]
- Stochastic analysis of lexical and semantic enhanced
structural language model
S. Wang, S. Wang, L. Cheng, R.
Greiner and D. Schuurmans
The 8th International Colloquium on
Grammatical Inference, ICGI-2006. [pdf]
- Combining statistical language models via the latent maximum
entropy principle
S. Wang, D. Schuurmans, F. Peng and Y.
Zhao
Machine Learning Journal: Special Issue on Learning in
Speech and Language Technologies, Vol. 60, pp. 229-250, 2005. [pdf]
Maximum entropy and extensions
- The latent maximum entropy principle
S. Wang,
D. Schuurmans and Y. Zhao. [pdf]
partial
results appeared at IEEE International Symposium on Information
Theory, ISIT-2002. [ps]
- Consistency and generalization bounds for maximum entropy
Estimation
S. Wang, R. Greiner and S. Wang. [abstract] [Long
Version][Short Version]
- Learning mixture models with the regularized latent maximum
entropy principle
S. Wang, D. Schuurmans, F. Peng and Y.
Zhao
IEEE Trans. on Neural Networks: Special Issue on Information
Theoretic Learning, Vol. 15, No. 4, pp. 903-916, 2004. [pdf]
- Learning continuous latent variable models with Bregman
divergence
S. Wang and D. Schuurmans
The 14th
International Conference on Algorithmic Learning Theory, ALT-2003.
[pdf]
Online learning
- Almost sure convergence of Titterington's recursive
estimator for finite mixture models
S. Wang and Y.
Zhao
Statistics & Probability Letters, Vol. 76, No. 18,
pp. 2001-2006, December 2006. [pdf]
- Implicit online learning with kernels
L. Cheng,
S. Vishwanathan, D. Schuurmans, S. Wang and T. Caelli
Advances in
Neural Information Processing Systems, NIPS-2006. [pdf]
Speech recognition
- On-line Bayesian tree-structured transformation of HMMs with
optimal model selection for speaker adaptation
S. Wang and
Y. Zhao
IEEE Trans. on Speech and Audio Processing, Vol. 9,
No. 6, pp. 663-677, September 2001. [pdf]
Power system economics
- Short-term generation scheduling with transmission and
environmental constraints using an augmented Lagrangian
relaxation
S. Wang, S. Shahidehpour, D. Kirschen, S.
Mokhtari and G. Irisarri
IEEE Trans. on Power Systems, Vol.
10, No. 3, pp. 1294-1301, August 1995. [pdf]
- Probabilistic marginal cost curve and its
applications
S. Wang, S. Shahidehpour and N.
Xiang
IEEE Trans. on Power Systems, Vol. 10, No. 3, pp
1321-1328, August 1995. [pdf]
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