2. Publications

Journal

1. W. Chu and S. S. Keerthi (2007)  Support vector ordinal regression,  Neural Computation 19(3):792-815 (View Abstract)

   In this paper, we propose two new support vector formulations for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.

2. W. Chu, Z. Ghahramani, A. Podtelezhnikov and D. L. Wild (2006) Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics 3(2):98-113 (View Abstract)

   In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. By incorporating the information from long range interactions in beta-sheets, this model is also capable of carrying out inference on contact maps. [ps][supplement]

3. W. Chu, Z. Ghahramani, F. Falciani and D. L. Wild (2005)  Biomarker discovery with Gaussian processes in microarray gene expression data,  Bioinformatics 2005(21):3385-3393 (View Abstract)

   In this paper, we describe a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian framework. [pdf] [ps] [code]

4. W. Chu and Z. Ghahramani (2005)  Gaussian processes for ordinal regression,  Journal of Machine Learning Research 6(Jul):1019-1041 (View Abstract)

   In this paper, we present a probabilistic approach to ordinal regression in Gaussian processes. In the Bayesian framework of Gaussian processes, we propose a likelihood function for ordinal variables that is a generalization of the probit function. Two inference techniques, based on Laplace approximation and expectation propagation respectively, are applied for model selection. [pdf] [ps] [zip] [code]

5. W. Chu, C. J. Ong and S. S. Keerthi (2005)  An improved conjugate gradient scheme to the solution of least squares SVM,  IEEE Transactions on Neural Networks 16(2):498-501 (View Abstract)

   In this paper, we propose an improved method to the numerical solution of LS-SVM. Compared with the existing algorithm (Suykens et al, 1999) for LS-SVM, our approach is about twice as efficient.

6. W. Chu, S. S. Keerthi and C. J. Ong (2004)  Bayesian support vector regression using a unified loss function,  IEEE Transactions on Neural Networks 15(1):29-44 (View Abstract)

   In this paper, we use soft insensitive loss function in likelihood evaluation, and describe a Bayesian framework in a stationary Gaussian process. Bayesian methods are used to implement model adaptation, while keeping the merits of support vector regression, such as quadratic programming and sparseness. Moreover, confidence interval is provided in prediction. [pdf] [ps] [zip] [code]

7. W. Chu, S. S. Keerthi and C. J. Ong (2003)  Bayesian trigonometric support vector classifier,  Neural Computation 15(9):2227-2254 (View Abstract)

   In this paper, we propose Bayesian support vector classifier by introducing a novel likelihood function, known as trigonometric likelihood function. Model adaptation and ARD feature selection could be implemented intrinsically in hyperparameter inference. Another benefit is the class probability in making predictions. [pdf] [code]

Refereed Conference/Workshop

8. Seung-Taek Park and W. Chu (2009) Pairwise preference regression for cold-start recommendation, to appear in Proc. of ACM Recommender Systems (View Abstract)

   to be added ...

9. W. Chu and Z. Ghahramani (2009) Probabilistic models for incomplete multi-dimensional arrays, International Conference on Artificial Intelligence and Statistics (AISTATS-12):89-96 (View Abstract)

   In multiway data, each sample is measured by multiple sets of correlated attributes. We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker. Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets. We verify the usefulness of this approach by comparing against classical models on applications to modeling amino acid fluorescence, collaborative filtering and a number of benchmark multiway array data. [pdf]

10. W. Chu and Seung-Taek Park (2009) Personalized recommendation on dynamic content using predictive bilinear models, International World Wide Web Conference (WWW-18):692-700 (View Abstract)

    In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches. [pdf] [slides]

11. W. Chu, et al. (2009) A case study of behavior-driven conjoint analysis on Yahoo! Front Page Today Module, to appear ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-15 Industry Track):1097-1104 (View Abstract)

    In this paper, we report a successful large-scale case study of conjoint analysis on click through stream in a real-world application at Yahoo!. We consider identifying usersĄŻ heterogenous preferences from millions of click/view events and building predictive models to classify new users into segments of distinct behavior pattern. A scalable conjoint analysis technique, known as tensor segmentation, is developed by utilizing logistic tensor regression in standard partworth framework for solutions. [pdf]

12. R. Silva, W. Chu and Z. Ghahramani (2007) Hidden common cause relations in relational learning, Neural Information Processing Systems (NIPS-20):1345-1352 (View Abstract)

    We consider the case when relationships are postulated to exist due to hidden common causes. We discuss how the resulting graphical model differs from Markov networks, and how it describes different types of real-world relational processes. A Bayesian nonparametric classification model is built upon this graphical representation and evaluated with several empirical studies. GOTO Ricardo Silva's homepage for [pdf], [data] and [code]

13. K. Yu and W. Chu (2007) Gaussian process models for link analysis and transfer learning, Neural Information Processing Systems (NIPS-20):1657-1664 (View Abstract)

    In this paper we model relational random variables on the edges of a network using Gaussian processes (GPs). We describe appropriate GP priors, i.e., covariance functions, for directed and undirected networks connecting homogeneous or heterogenous nodes. The framework suggests an intimate connection between link prediction and transfer learning, which were traditionally two separate topics. [pdf]

14. P. K. Shivaswamy, W. Chu and M. Jansche (2007) A support vector approach to censored targets, IEEE International Conference on Data Mining (ICDM-07):655-660 (View Abstract)

    Censored targets, such as the time to events in survival analysis, can generally be represented by intervals on the real line. In this paper, we propose a novel support vector technique (named SVCR) for regression on censored targets. Interestingly, this approach provides a general formulation for both standard regression and binary classification tasks. [pdf] [longer version]

15. V. Sindhwani, W. Chu and S. S. Keerthi (2007) Semi-supervised Gaussian process classifiers, International Joint Conferences on Artificial Intelligence (IJCAI-07):1059-1064 (View Abstract)

    We consider the problem of utilizing unlabeled data for Gaussian process inference. Using a geometrically motivated data-dependent prior, we propose a graph-based construction of semi-supervised Gaussian processes. We demonstrate this approach empirically on several classification problems. [pdf]

16. W. Chu, V. Sindhwani, Z. Ghahramani and S. S. Keerthi (2006) Relational learning with Gaussian processes, Neural Information Processing Systems (NIPS-19):289-296 (View Abstract)

    Correlation between instances is often modelled via a kernel function using input attributes of the instances. Relational knowledge can further reveal additional pairwise correlations between variables of interest. In this paper, we develop a class of models which incorporates both reciprocal relational information and input attributes using Gaussian process techniques. This approach provides a novel non-parametric Bayesian framework with a data-dependent prior for supervised learning tasks. We also apply this framework to semi-supervised learning. Experimental results on several real world data sets verify the usefulness of this algorithm. [pdf]

17. K. Yu, W. Chu, S. Yu, V. Tresp and Z. Xu (2006) Stochastic relational models for discriminative link prediction, Neural Information Processing Systems (NIPS-19):1553-1560 (View Abstract)

    We introduce a Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e., links) via an interplay of multiple GPs, each defined on one type of entities. [pdf]

18. S. K. Shevade and W. Chu (2006) Minimum enclosing spheres formulations for support vector ordinal regression, IEEE International Conference on Data Mining (ICDM-06):1054-1058 (View Abstract)

    We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. [pdf]

19. W. Chu, Z. Ghahramani, R. Krause and D. L. Wild  (2006)  Identifying protein complexes in high-throughput protein interaction screens using an infinite latent feature model, Pacific Symposium on Biocomputing (PSB-06):231-242

20. W. Chu (2006)  Model selection: An empirical study on two kernel classifiers, International Joint Conference on Neural Networks (IJCNN-06):1673-1679

21. W. Chu and Z. Ghahramani (2005)  Extensions of Gaussian processes for ranking: semi-supervised and active learning, Workshop Learning to Rank at (NIPS-18):29-34 (View Abstract)

    Unlabelled examples in supervised learning tasks can be optimally exploited using semi-supervised methods and active learning. We focus on ranking learning from pairwise instance preference to discuss these important extensions, semi-supervised learning and active learning, in the probabilistic framework of Gaussian processes. [ps]

22. S. S. Keerthi and W. Chu (2005)  A matching pursuit approach to sparse Gaussian process regression, Neural Information Processing Systems (NIPS-18):643-650 (View Abstract)

    In this paper, we propose a new basis selection criterion for building sparse GP regression models that provides promising gains in accuracy as well as efficiency over previous methods. Our algorithm is much faster than that of Smola and Bartlett, while, in generalization it greatly outperforms the information gain approach proposed by Seeger et al, especially on the quality of predictive distributions. [ps] [code]

23. W. Chu and Z. Ghahramani (2005)  Preference learning with Gaussian processes, International Conference on Machine Learning (ICML-05):137-144 (View Abstract)

    In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relations in the Bayesian framework. The generalized formulation is also applicable to tackle many multiclass problems. [pdf] [ps] [zip] [code]

24. W. Chu and S. S. Keerthi (2005)  New approaches to support vector ordinal regression,  International Conference on Machine Learning (ICML-05):145-152 (View Abstract)

    In this paper, we propose two new support vector formulations for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. [pdf] [ps] [zip] [code]

25. W. Chu, Z. Ghahramani and D. L. Wild (2004)  A graphical model for protein secondary structure prediction,  International Conference on Machine Learning (ICML-04):161-168 (View Abstract)

    In this paper, we present a graphical model that extends segmental semi-Markov models (SSMM) to exploit multiple sequence alignment profiles for protein structure prediction. A novel parameterized model is proposed as the likelihood function for the SSMM. By incorporating the information from long range interactions in beta-sheets, this model is capable of carrying out inference on contact maps. [pdf] [ps] [zip] [webserver]

26. W. Chu, Z. Ghahramani and D. L. Wild (2004)  Protein secondary structure prediction using sigmoid belief networks to parameterize segmental semi-Markov models,  European Symposium on Artificial Neural Networks (ESANN-04):81-86

27. K. Duan, S. S. Keerthi, W. Chu, S. K. Shevade and A. N. Poo  (2003)  Multi-category classification by soft-max combination of binary classifiers,  International Workshop on Multiple Classifier Systems (MCS-03):125-134

28. S. S. Keerthi, et al. (2002)  A machine learning approach for the curation of Biomedical literature - KDD Cup 2002 (Task 1),  SIGKDD Explorations Newsletter, 4(2)

29. W. Chu, S. S. Keerthi and C. J. Ong (2002)  A general formulation for support vector machines,  International Conference on Neural Information Processing (ICONIP-02)

30. W. Chu, S. S. Keerthi and C. J. Ong (2002)  A new Bayesian design method for support vector classification,  International Conference on Neural Information Processing (ICONIP-02)

31. W. Chu, S. S. Keerthi and C. J. Ong (2001)  A unified loss function in Bayesian framework for support vector regression,  International Conference on Machine Learning (ICML-01):51-58

In Book

32. W. Chu, S. S. Keerthi, C. J. Ong and Z. Ghahramani (2006)  Bayesian support vector machines for feature ranking and selection,  In I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors, Feature Extraction, Foundations and Applications

Thesis

33. W. Chu (2003)  Bayesian approach to support vector machines, Doctoral Dissertation, National University of Singapore (View Abstract)

    In this thesis, we develop Bayesian support vector machines for regression and classification. This work can also be regarded as support vector variants of Gaussian processes. [pdf] [zip] [code]

email : chuwei at gatsby.ucl.ac.uk 2005.12.20