research


I have now graduated with a PhD in Computer Science specializing in Probabilistic Reasoning in Artificial Intelligence and currently am a research scientist at Google. Here is a Google research blog post about one of the systems I co-designed.

During the four years that I was at Stanford I worked on a couple of related topics:
  • Active Learning. The standard framework in Machine Learning presents the learner with a randomly sampled data set. There has been growing interest in the area of Active Learning. Here, one allows the learner the flexibility to choose the data points that it feels are most relevant for learning a particular task. One analogy is that a standard passive learner is a student that sits and listens to a teacher while an active learner is a student that asks the teacher questions, listens to the answers and asks further questions based upon the teacher's response. I investigated techniques for performing active learning in three widely applicable situations: classification, density estimation and causal discovery. Our results showed that active learners using these techniques can outperfom regular passive learners substantially - particularly in the text classification and image retrieval domains (very relevant domains given the recent explosion of readily available data from the internet).

  • Restricted Bayes Classifiers. Support Vector Classifiers are a core technology in modern machine learning. They have strong theoretical justifications and have shown empirical successes. My research led me to attempt to cast them in the probabilistic framework. The technique for achieving this appears applicable to a wider range of classifiers.
    		• Prof Daphne Koller (My advisor)

    Daphne's Research Group Homepage

    A list of publications about Support Vector Machines


    Here is a link to my publication and citations on Google Scholar.

     

    Thesis

  • Active Learning: Theory and Applications. Simon Tong. Stanford University 2001. (gzip). Also available as (PS) and (PDF).

     

    • Publications in Refereed Journals

  • Support Vector Machine Concept-Dependent Active Learning For Image Retrieval. Edward Chang, Simon Tong, Kingsby Goh, Chang-Wei Chang IEEE Transactions on Multimedia 2005.

  • Support Vector Machine Active Learning with Applications to Text Classification. Simon Tong, Daphne Koller. Journal of Machine Learning Research. Volume 2, pages 45-66. 2001.. (gzip PS). Also available as (PS).

  • Three-point linkage analysis in crosses of allogamous plant species. M.S Ridout, S. Tong, C. J. Vowden and K. R. Tobutt. Genetical Research , (1998) 72, pp. 111-121. (Gif)

     

    • Publications in Refereed Conferences
  • Support Vector Machine Active Learning for Image Retrieval. Simon Tong, Edward Chang. To appear in ACM Multimedia 2001. (gzip PS) (Demo)

  • Active Learning for Structure in Bayesian Networks. Simon Tong, Daphne Koller International Joint Conference on Artificial Intelligence 2001. (PS)

  • Active Learning for Parameter Estimation in Bayesian Networks. Simon Tong, Daphne Koller. To appear in Neural Information Processing Systems 2000. (PS)

  • Support Vector Machine Active Learning with Applications to Text Classification. Simon Tong, Daphne Koller. Proceedings of the Seventeenth International Conference on Machine Learning. 2000. (gzip PS). Also available as (PS).

  • Restricted Bayes Optimal Classifiers. Simon Tong, Daphne Koller. AAAI 2000. (PS)

     

    • Workshop Publications
  • Bayes Optimal Hyperplanes -> Maximal Margin Hyperplanes. Simon Tong, Daphne Koller IJCAI Workshop on Support Vector Machines 1999. (PS)

     

    • Presentations
  • Active Learning for Structure in Bayesian Networks. Simon Tong, Daphne Koller. IJCAI 2001. (Html).

  • Active Learning for Parameter Estimation in Bayesian Networks. Simon Tong, Daphne Koller. NIPS 2000. (Html).

  • Support Vector Machine Active Learning with Applications to Text Classification. Simon Tong, Daphne Koller. Proceedings of the Seventeenth International Conference on Machine Learning. 2000. (Html).

  • Bayes Optimal Hyperplanes -> Maximal Margin Hyperplanes. Simon Tong, Daphne Koller Presented at the SnowBird Learning Workshop, April 1999. (Html)








    • simon.tong@cs.stanford.edu. 2001.
    Computer Science Dept, Stanford University, Stanford, CA 94305-9010