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• Book • Book Chapters • Journal Papers • Top Conferences • Other •

Book

Gökhan Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar, and S. V. N. Vishwanathan, editors. Predicting Structured Data, MIT Press, Cambridge, Massachusetts, 2007. in press
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Book Chapters

S. V. N. Vishwanathan and Alexander J. Smola. Fast Kernels for String and Tree Matching. In Kernels and Bioinformatics, MIT Press, Cambridge, MA, 2004.
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S. V. N. Vishwanathan and M. N. Murty. Use of MPSVM for Data Set Reduction. In A. Abraham, L. Jain, and J. Kacprzyk, editors, Recent Advances in Intelligent Paradigms and Applications, Studies in Fuzziness and Soft Computing, Springer Verlag, Berlin, November 2002.
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Journal Papers

Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, and S. V. N. Vishwanathan. Hash Kernels for Structured Data. Journal of Machine Learning Research, 2009. To appear
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Choon Hui Teo, S. V. N. Vishwanathan, Alex J. Smola, and Quoc V. Le. Bundle Methods for Regularized Risk Minimization. Journal of Machine Learning Research, 2009. To appear
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S. V. N. Vishwanathan, Nicol N. Schraudolph, Imre Risi Kondor, and Karsten M. Borgwardt. Graph Kernels. Journal of Machine Learning Research, 2009. First version: May 2008. Revised version: April 2009. To appear.
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Manfred K. Warmuth and S. V. N. Vishwanathan. Leaving the Span. Journal of Machine Learning Research, 2009. Submitted in February 2009
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S. V. N. Vishwanathan and Li Cheng. Implicit Online Learning with Kernels. Journal of Machine Learning Research, 2008.
Submitted in September 2008
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Jin Yu, S. V. N. Vishwanathan, Simon Günter, and Nicol N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization. Journal of Machine Learning Research, 2008.
Submitted (November 2008)
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Simon Günter, Nicol N. Schraudolph, and S. V. N. Vishwanathan. Fast Iterative Kernel Principal Component Analysis. Journal of Machine Learning Research, 8:1893–1918, August 2007.
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S. V. N. Vishwanathan, Alexander J. Smola, and René Vidal. Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. Intl. Journal of Computer Vision, 73(1):95–119, Springer-Verlag, Netherlands, 2007.
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S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, and Alexander J. Smola. Kernel Extrapolation. Neurocomputing, 69(7-9):721–729, 2006.
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S. V. N. Vishwanathan, Nicol N. Schraudolph, and Alexander J. Smola. Step Size Adaptation in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research, 7:1107–1133, June 2006.
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Gaelle Loosli, Stephané Canu, S. V. N. Vishwanathan, Alexander J. Smola, and Manojit Chattopadhyay. Bo\^ite á outils SVM simple et rapide. RIA - Revue d'intelligence artificielle, 2005.
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S. V. N. Vishwanathan and M. N. Murty. Kohonen's SOM with Cache. Pattern Recognition, 33(11):1927–1929, November 2000.
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Top Conferences

Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt. Efficient Graphlet Kernels for Large Graph Comparison . In Proceedings of International Workshop on Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics, 2009. (84 out of 210, 40% acceptance rate)
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Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, and S. V. N. Vishwanathan . Hash Kernels. In Proceedings of International Workshop on Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics, 2009. (84 out of 210, 40% acceptance rate)
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Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, and Nicol N. Schraudolph. Variable Metric Stochastic Approximation Theory. In Proceedings of International Workshop on Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics, 2009. (84 out of 210, 40% acceptance rate)
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Jin Yu, S. V. N. Vishwanathan, and Jian Zhang. The Entire Quantile Path of a Risk-Agnostic SVM Classifier. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), Montreal, Canada, June 2009. (76 out of 243, 31\% acceptance rate)
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Li Cheng, S. V. N. Vishwanathan, and Xinhua Zhang. Consistent Image Analogies using Semi-supervised Learning. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society, Anchorage, Alaska (USA), June 2008. (508 out of 1593, 32% acceptance rate)
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Manfred K. Warmuth, Karen A. Glocer, and S. V. N. Vishwanathan. Entropy Regularized LPBoost. In Proc. Intl. Conf. Algorithmic Learning Theory, pp. 256 – 271, Springer-Verlag, Budapest, October 2008. (31 out of xx, xx% acceptance rate)
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Jin Yu, S. V. N. Vishwanathan, Simon Günter, and Nicol N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proc. Intl. Conf. Machine Learning, Helsinki, Finland, July 2008. (155 out of 583, 26.5% acceptance rate)
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Karsten M. Borgwardt, Hans-Peter Kriegel, S. V. N. Vishwanathan, and Nicol N. Schraudolph. Graph kernels for disease outcome prediction from protein-protein interaction networks. In Proceedings of the Pacific Symposium of Biocomputing 2007, World Scientific, Maui Hawaii, January 2007.
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Li Cheng and S. V. N. Vishwanathan. Learning to Compress Images and Video. In Proc. Intl. Conf. Machine Learning, pp. 161–168, June 2007. (152 out of 522, 29% acceptance rate)
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Qinfeng Shi, Yasemin Altun, Alexander J. Smola, and S. V. N. Vishwanathan. Semi-Markov Models for Sequence Segmentation. In Proceedings of the 2007 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 640–648, 2007. (66 out of 398, 16.5% acceptance rate)
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Alexander J. Smola, S. V. N. Vishwanathan, and Quoc V. Le. Bundle Methods for Machine Learning. In Advances in Neural Information Processing Systems 20, MIT Press, Cambridge MA, 2007. (217 out of 975, 22% acceptance rate)
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Choon Hui Teo, Quoc V. Le, Alexander J. Smola, and S. V. N. Vishwanathan. A Scalable Modular Convex Solver for Regularized Risk Minimization. In Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2007. (100 out of 500, 20% acceptance rate)
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Xinhua Zhang, Douglas Aberdeen, and S. V. N. Vishwanathan. Conditional Random Fields for Multi-Agent Reinforcement Learning. In Proc. Intl. Conf. Machine Learning, pp. 1143–1150, June 2007. best student paper award, (152 out of 522, 29% acceptance rate)
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Karsten M. Borgwardt, S. V. N. Vishwanathan, and Hans-Peter Kriegel. Class prediction from time series gene expression profiles using dynamical systems kernels. In Proceedings of the Pacific Symposium of Biocomputing 2006, pp. 547 – 558, World Scientific, Maui Hawaii, January 2006.
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Li Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, and Terry Caelli. Implicit Online Learning With Kernels. In Advances in Neural Information Processing Systems 19, MIT Press, Cambridge MA, 2006. (204 out of 833, 24.5% acceptance rate)
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Thomas Gärtner, Quoc V. Le, Simon Burton, Alexander J. Smola, and S. V. N. Vishwanathan. Large-Scale Multiclass Transduction. In Advances in Neural Information Processing Systems 18, pp. 411 – 418, MIT Press, Cambride, MA, 2006. (206 out of 753, 27.5% acceptance rate)
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Nicol N. Schraudolph, Simon Günter, and S. V. N. Vishwanathan. Fast Iterative Kernel PCA. In Advances in Neural Information Processing Systems 19, MIT Press, Cambridge MA, 2006. (204 out of 833, 24.5% acceptance rate)
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Choon Hui Teo and S. V. N. Vishwanathan. Fast and space efficient string kernels using suffix arrays. In Proc. Intl. Conf. Machine Learning, pp. 929–936, ACM Press, New York, NY, USA, 2006. (140 out of 700, 20% acceptance rate)
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S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph. Fast Computation of Graph Kernels. In Advances in Neural Information Processing Systems 19, MIT Press, Cambridge MA, 2006. (204 out of 833, 24.5% acceptance rate)
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S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark Schmidt, and Kevin Murphy. Accelerated Training Conditional Random Fields with Stochastic Gradient Methods. In Proc. Intl. Conf. Machine Learning, pp. 969 – 976, ACM Press, New York, NY, USA, 2006. (140 out of 700, 20% acceptance rate)
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Karsten M. Borgwardt, C. S. Ong, S. Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, and Hans-Peter Kriegel. Protein Function Prediction via Graph Kernels. In Proceedings of Intelligent Systems in Molecular Biology (ISMB), Detroit, USA, 2005. (13% acceptance rate)
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Omri Guttman, S. V. N. Vishwanathan, and Robert C. Williamson. Probabilistic Automata Learning via Oracles. In Proc. Intl. Conf. Algorithmic Learning Theory, pp. 171 – 182, Springer-Verlag, Singapore, October 2005. (30 out of 98, 30% acceptance rate)
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Alexander J. Smola, S. V. N. Vishwanathan, and Thomas Hofmann. Kernel Methods for Missing Variables. In Proceedings of International Workshop on Artificial Intelligence and Statistics, pp. 325–332, Society for Artificial Intelligence and Statistics, 2005. (21 out of 150, 14% acceptance rate)
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S. V. N. Vishwanathan and Alexander J. Smola. Binet-Cauchy Kernels. In Advances in Neural Information Processing Systems 17, pp. 1441 – 1448, MIT Press, Cambridge, MA, 2005. (207 out of 822, 25% acceptance rate)
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Manfred K. Warmuth and S. V. N. Vishwanathan. Leaving the Span. In Proc. Annual Conf. Computational Learning Theory, pp. 365 – 380, Springer-Verlag, Bertinoro, Italy, June 2005. (45 out of 120, 37.5% acceptance rate)
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Alexander J. Smola, S. V. N. Vishwanathan, and Eleazar Eskin. Laplace Propogation. In Advances in Neural Information Processing Systems 16, pp. 441–448, MIT Press, Cambridge, MA, 2004. (198 out of 717, 27.5% acceptance rate)
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S. V. N. Vishwanathan and Alexander J. Smola. Fast Kernels for String and Tree Matching. In S. Becker, Sebastian Thrun, and Klaus Obermayer, editors, Advances in Neural Information Processing Systems 15, pp. 569–576, MIT Press, Cambridge, MA, 2003. (221 out of 710, 31.1% acceptance rate)
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S. V. N. Vishwanathan, Alexander J. Smola, and M. N. Murty. SimpleSVM. In Proc. Intl. Conf. Machine Learning, AAAI press, Washington DC, 2003. (119 out of 371, 32% acceptance rate)
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S. V. N. Vishwanathan and M. N. Murty. Geometric SVM: A fast and intuitive SVM algorithm. In Proc. Intl. Conf. Pattern Recognition, 2002.
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Other

Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, and Alexander J. Smola. Joint Regularization. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2005), Brugge, Belgium, 2005.
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A. Karatzoglou, S. V. N. Vishwanathan, Nicol N. Schraudolph, and Alexander J. Smola. Step size-Adapted Online Support Vector Learning. In Proceedings of ISSPA 2005, Australia, August 2005.
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Gaelle Loosli, Stephané Canu, S. V. N. Vishwanathan, and Alexander J. Smola. Invariances in Classification: an efficient SVM implementation. In ASMDA 2005 - Applied Stochastic Models and Data Analysis, 2005.
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Gaelle Loosli, Stephané Canu, S. V. N. Vishwanathan, Alexander J. Smola, and Manojit Chattopadhyay. Une bo\^ite á outils rapide et simple pour les SVM. In CAp 2004 - Conférence d'Apprentissage, pp. 113–128, Presses Universitaires de Grenoble, 2004.
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Alexander J. Smola and S. V. N. Vishwanathan. Hilbert space embeddings in dynamical systems. In Proceedings of the 13th IFAC symposium on system identification, Rotterdam, Netherlands, August 2003.
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Alexander J. Smola and S. V. N. Vishwanathan. Cholesky Factorization for Rank-$k$ Modifications of Diagonal Matrices. SIAM Journal of Matrix Analysis, 2003. In Preparation
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S. V. N. Vishwanathan and M. N. Murty. Jigsawing: A Method to Generate Virtual Examples in OCR Data. In Hybrid Intelligent Systems, 2002.
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S. V. N. Vishwanathan and M. N. Murty. SSVM: A Simple SVM Algorithm. In Proc. Intl. Joint Conf. on Neural Networks, IEEE Press, 2002.
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S. V. N. Vishwanathan and M. N. Murty. Use of MPSVM for Data Set Reduction. In Hybrid Information Systems, Physica Verlag, Heidelberg, 2001.
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