Publications

• 2012

  • A. Agarwal, O. Chapelle, M. Dudik, and J. Langford. A reliable effective terascale linear learning system. CoRR, abs/1110.4198, 2012. Code for the splice site data.
  • O. Chapelle and L. Li. An empirical evaluation of thompson sampling. Advances in Neural Information Processing Systems 24, 2012.
  • O. Chapelle, T. Joachims, F. Radlinski, and Y. Yue. Large scale validation and analysis of interleaved search evaluation. ACM Transactions on Information Systems, 30(1), 2012.
  • M. Chen, Z. Xu, K. Weinberger, O. Chapelle, and D. Kedem. Classifier cascade: Tradeoff between accuracy and feature evaluation cost. In 15th International Conference on Artificial Intelligence and Statistics ( AISTATS ), 2012.
  • P.S. Dhillon, S.S. Keerthi, K. Bellare, O. Chapelle, S. Sundararajan, and CA Santa Clara. Deterministic annealing for semi-supervised structured output learning. In 15th International Conference on Artificial Intelligence and Statistics ( AISTATS ), 2012.
  • U. Ozertem, O. Chapelle, P. Donmez, and E. Velipasaoglu. Learning to suggest: A machine learning framework for ranking query suggestions. In SIGIR '12: Proceedings of the 35th annual international ACM SIGIR conference on Research and development in information retrieval, 2012.

• 2011

  • O. Chapelle and Y. Chang. Yahoo! learning to rank challenge overview. JMLR Workshop and Conference Proceedings, 14:1–24, 2011.
  • O. Chapelle and D. Erhan. Improved preconditioner for hessian free optimization. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
  • O. Chapelle, Y. Chang, and T.-Y. Liu, editors. Proceedings of the Yahoo! Learning to Rank Challenge, volume 14 of JMLR Workshop and Conference Proceedings, 2011.
  • O. Chapelle, S. Ji, C. Liao, E. Velipasaoglu, L. Lai, and S.-L. Wu. Intent-based diversification of web search results: Metrics and algorithms. Information Retrieval Journal, 14(6):572–592, 2011.
  • O. Chapelle, P. Shivaswamy, S. Vadrevu, K. Weinberger, Y. Zhang, and B. Tseng. Boosted multi-task learning. Machine Learning Journal, 85(1-2):149–173, 2011.

• 2010

  • J. Abernethy, O. Chapelle, and C. Castillo. Graph regularization methods for web spam detection. Machine Learning Journal, 81(2):207–225, 2010.
  • B. Bai, J. Weston, D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, and K. Weinberger. Learning to rank with (a lot of) word features. Information Retrieval Journal, 13(3):291–314, 2010.
  • B. Cambazoglu, H. Zaragoza, O. Chapelle, J. Chen, C. Liao, Z. Zheng, and J. Degenhardt. Early exit optimizations for additive machine learned ranking systems. In WSDM '10: Proceedings of the Third ACM International Conference on Web Search and Data Mining, 2010.
  • O. Chapelle and S. S. Keerthi. Efficient algorithms for ranking with SVMs. Information Retrieval Journal, 13(3):201–215, 2010.
  • O. Chapelle and M. Wu. Gradient descent optimization of smoothed information retrieval metrics. Information Retrieval Journal, 13(3):216–235, 2010.
  • O. Chapelle, P. Shivaswamy, S. Vadrevu, K. Weinberger, Y. Zhang, and B. Tseng. Multi-task learning for boosting with application to web search ranking. In KDD '10: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.
  • B. Long, O. Chapelle, Y. Zhang, Y. Chang, Z. Zheng, and B. Tseng. Active learning for ranking through expected loss optimization. In SIGIR '10: Proceedings of the 33nd annual international ACM SIGIR conference on Research and development in information retrieval, 2010.
  • Y. Yue, Y. Gao, O. Chapelle, Y. Zhang, and T. Joachims. Learning more powerful test statistics for click-based retrieval evaluation. In SIGIR '10: Proceedings of the 33nd annual international ACM SIGIR conference on Research and development in information retrieval, 2010.

• 2009

  • B. Bai, J. Weston, D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, and K. Weinberger. Supervised semantic indexing. In CIKM '09: Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009.
  • O. Chapelle and Y. Zhang. A dynamic bayesian network click model for web search ranking. In Proceedings of the 18th International World Wide Web Conference (WWW), 2009.
  • O. Chapelle, D. Metlzer, Y. Zhang, and P. Grinspan. Expected reciprocal rank for graded relevance. In CIKM '09: Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009.
  • C. B. Do, Q. Le, C. H. Teo, O. Chapelle, and A. Smola. Tighter bounds for structured estimation. In Advances in Neural Information Processing Systems 21. MIT Press, 2009.
  • S. Ji, K. Zhou, C. Liao, Z. Zheng, G.-R. Xue, O. Chapelle, G. Sun, and H. Zha. Global ranking by exploiting user clicks. In SIGIR '09: Proceedings of the 32nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2009.
  • Q. V. Le, A. Smola, O. Chapelle, and C. H. Teo. Optimization of ranking measures. unpublished, 2009.
  • K. Weinberger and O. Chapelle. Large margin taxonomy embedding with an application to document categorization. In Advances in Neural Information Processing Systems 21. MIT Press, 2009.

• 2008

  • J. Abernethy, O. Chapelle, and C. Castillo. Webspam identification through content and hyperlinks. In Adversarial Information Retrieval on the Web (AIRWEB). ACM Press, April 2008.
  • B. Carterette, P.N. Bennett, and O. Chapelle. A test collection of preference judgments. In SIGIR 2008 Workshops: Beyond Binary Relevance: Preferences, Diversity, and Set-Level Judgments, 2008.
  • O. Chapelle and S. S. Keerthi. Multi-class feature selection with support vector machines. In Proceedings of the American Statistical Association, 2008.
  • O. Chapelle and A. Rakotomamonjy. Second order optimization of kernel parameters. In NIPS Workshop on Automatic Selection of Optimal Kernels, 2008.
  • O. Chapelle, V. Sindhwani, and S. S. Keerthi. Optimization techniques for semi-supervised support vector machines. Journal of Machine Learning Research, 9:203–233, 2008.
  • F. Sinz, O. Chapelle, A. Agarwal, and B. Schölkopf. An analysis of inference with the universum. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, 2008.
  • C. Walder and O. Chapelle. Learning with transformation invariant kernels. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, 2008.
  • Z. Zheng, H. Zha, T. Zhang, O. Chapelle, K. Chen, and G. Sun. A general boosting method and its application to learning ranking functions for web search. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, 2008.

• 2007

  • L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, editors. Large Scale Kernel Machines. MIT Press, Cambridge, MA., 2007.
  • O. Chapelle, Q. Le, and A. Smola. Large margin optimization of ranking measures. In NIPS workshop on Machine Learning for Web Search, 2007.
  • O. Chapelle, V. Sindhwani, and S. S. Keerthi. Branch and bound for semi-supervised support vector machines. In Advances in Neural Information Processing Systems 19, pages 217–224, 2007. Matlab code.
  • O. Chapelle. Training a support vector machine in the primal. Neural Computation, 19(5):1155–1178, 03 2007. An updated version appeared in the Large Scale Kernel Machines book.
  • P. Gehler and O. Chapelle. Deterministic annealing for multiple-instance learning. In Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.
  • S. Keerthi, V. Sindhwani, and O. Chapelle. An efficient method for gradient-based adaptation of hyperparameters in SVM models. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 673–680. MIT Press, 2007.
  • C. Walder, B. Schölkopf, and O. Chapelle. Implicit surfaces with globally regularised and compactly supported basis functions. In Advances in Neural Information Processing Systems 19, Cambridge, MA, 2007. MIT Press.

• 2006

  • G. C. Cawley, N. L.C. Talbot, and O. Chapelle. Estimating predictive variances with kernel ridge regression. In J. Quinonero Candela, I. Dagan, B. Magnini, and F. DAlché Buc, editors, Machine learning challenges: evaluating predictive uncertainty, visual object classification, and recognising textual entailment, Lecture Notes in Computer Science ; 3944, pages 56–77. Springer, 2006.
  • O. Chapelle, M. Chi, and A. Zien. A continuation method for semi-supervised SVMs. In Proceedings of the 23rd International Conference on Machine Learning, pages 185–192. ACM Press, 2006.
  • O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. Adaptive computation and machine learning. MIT Press, Cambridge, Mass., USA, 2006.
  • S. Keerthi, O. Chapelle, and D. Decoste. Building support vector machines with reduced classifier complexity. Journal of Machine Learning Research, 7:1493–1515, 07 2006.
  • T. N. Lal, O. Chapelle, and B. Schölkopf. Combining a filter method with SVMs. In I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, editors, Feature extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing ; 207, pages 439–445. Springer, 2006.
  • T. N. Lal, O. Chapelle, J. Weston, and A. Elisseeff. Embedded methods. In I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, editors, Feature Extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing ; 207, pages 137–165. Springer, 2006.
  • V. Sindhwani, S. Keerthi, and O. Chapelle. Deterministic annealing for semi-supervised kernel machines. In Proceedings of the 23rd International conference on machine learning, pages 841 – 848, 2006.
  • C. Walder, B. Schölkopf, and O. Chapelle. Implicit surface modelling with a globally regularised basis of compact support. Computer Graphics Forum, 25(3):635–644, 09 2006.

• 2005

  • O. Chapelle and Z. Harchaoui. A machine learning approach to conjoint analysis. In Y. Weiss Saul, L.K. and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 257–264, Cambridge, MA, USA, 2005. MIT Press.
  • O. Chapelle and A. Zien. Semi-supervised classification by low density separation. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 57–64, 2005.
  • O. Chapelle. Active learning for parzen window classifier. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 49–56, 2005.
  • A. Kowalczyk and O. Chapelle. An analysis of the anti-learning phenomenon for the class symmetric polyhedron. In Algorithmic Learning Theory: 16th International Conference, pages 78–92, 10 2005.
  • C. Walder, O. Chapelle, and B. Schölkopf. Implicit surface modelling as an eigenvalue problem. In S. Wrobel De Raedt, L., editor, Proceedings of the 22nd International Conference on Machine Learning, pages 937 – 944, 2005.

• 2004

  • O. Bousquet, O. Chapelle, and M. Hein. Measure based regularization. In L. Saul Thrun, S. and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, December 2004.
  • J. Eichhorn and O. Chapelle. Object categorization with SVM: kernels for local features. Technical Report 137, Max Planck Institute for Biological Cybernetics, 2004.
  • H. Fröhlich, O. Chapelle, and B. Schölkopf. Feature selection for support vector machines using genetic algorithms. International Journal on Artificial Intelligence Tools, 13(4):791–800, 2004.

• 2003

  • O. Chapelle, B. Schölkopf, and J. Weston. Semi-supervised learning through principal directions estimation. In ICML Workshop, The Continuum from Labeled to Unlabeled Data in Machine Learning & Data Mining, 2003.
  • O. Chapelle, J. Weston, and B. Schölkopf. Cluster kernels for semi-supervised learning. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 585–592. MIT Press, 2003.
  • H. Fröhlich, O. Chapelle, and B. Schölkopf. Feature selection for support vector machines by means of genetic algorithms. In 15th IEEE International Conference on Tools with AI, pages 142–148, 2003.
  • J. Weston, O. Chapelle, A. Elisseeff, B. Schölkopf, and V. Vapnik. Kernel dependency estimation. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 873–880. MIT Press, 2003.
  • J. Weston, F. Pérez-Cruz, O. Bousquet, O. Chapelle, A. Elisseeff, and B. Schölkopf. Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics, 19(6):764–771, 04 2003.

• 2002

  • O. Chapelle and B. Schölkopf. Incorporating invariances in nonlinear SVMs. In T. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, volume 14, pages 609–616. MIT Press, 2002. Longer version published as a technical report.
  • O. Chapelle, V. Vapnik, and Y. Bengio. Model selection for small sample regression. Machine Learning, 48(1-3):9–23, 2002.
  • O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1):131–159, 2002.
  • O. Chapelle. Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge. PhD thesis, LIP6, 2002.

• 2001

  • O. Chapelle, J. Weston, L. Bottou, and V. Vapnik. Vicinal risk minimization. In Advances in Neural Information Processing Systems 13, 2001.
  • J. Weston, O. Chapelle, and I. Guyon. Data cleaning with support vector machines. Technical report, Biowulf Technologies, 2001.
  • J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for support vector machines. In Advances in Neural Information Processing Systems 13, 2001.

• 2000

  • O. Chapelle and V. Vapnik. Model selection for support vector machines. In Advances in Neural Information Processing Systems 12, 2000.
  • O. Chapelle, V. Vapnik, and J. Weston. Transductive inference for estimating values of functions. In Advances in Neural Information Processing Systems 12, 2000.
  • V. Vapnik and O. Chapelle. Bounds on error expectation for support vector machines. Neural Computation, 12(9):2013–2036, 2000. Appeared also in Advances in Large Margin Classifiers.

• 1999

  • O. Chapelle, P. Haffner, and V. N. Vapnik. Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks, 10(5):1055–1064, Sept 1999.

• 1998

  • O. Chapelle. Support vector machines for image classification. Master's thesis, École Normale Superieure de Lyon, 1998.

• O. Chapelle and Y. Chang. Yahoo! learning to rank challenge overview. JMLR Workshop and Conference Proceedings, 14:1–24, 2011.
• O. Chapelle, Y. Chang, and T.-Y. Liu, editors. Proceedings of the Yahoo! Learning to Rank Challenge, number 14 in JMLR Workshop and Conference Proceedings, 2011.