View my publications...

by Type

BookBook ChaptersJournal PapersTop ConferencesOtherUnspecified

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.
Download: (unavailable)

Book Chapters

S. V. N. Vishwanathan. Machine Learning. In A.-H. El-Shaarawi and W. Piegorsch, editors, Encyclopedia of Environmetrics, pp. 1521–1524, John Wiley and Sons, 2012.
Download: (unavailable)

S. V. N. Vishwanathan and Alexander J. Smola. Fast Kernels for String and Tree Matching. In Kernel Methods in Computational Biology, MIT Press, Cambridge, MA, 2004.
Download: [pdf] [ps.gz] 

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.
Download: [pdf] [ps.gz] 

Journal Papers

Nan Ding, S. V. N. Vishwanathan, Manfred K. Warmuth, and Vasil Denchev. $t$-logistic regression. Journal of Machine Learning Research, September 2013. Submitted
Download: (unavailable)

Xinhua Zhang, Ankan Saha, and S. V. N. Vishwanathan. Accelerated Training of Max-Margin Markov Networks with Kernels. Theoretical Computer Science, 2013. To appear
Download: [pdf] [ps.gz] 

Bharath Hariharan, S. V. N. Vishwanathan, and Manik Varma. Efficient Max-Margin Multi-Label Classification with Applications to Zero-Shot Learning. Machine Learning, 88:127–155, July 2012.
Download: [pdf] [ps.gz] 

Xinhua Zhang, Ankan Saha, and S. V. N. Vishwanathan. Smoothing Multivariate Performance Measures. Journal of Machine Learning Research, 13:3589–3646, December 2012.
Download: [pdf] [ps.gz] 

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, 11:311–365, January 2010.
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan, Nicol N. Schraudolph, Imre Risi Kondor, and Karsten M. Borgwardt. Graph Kernels. Journal of Machine Learning Research, 11:1201–1242, April 2010.
Download: [pdf] [ps.gz] 

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, 11:1145–1200, March 2010.
Download: [pdf] [ps.gz] 

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, 10:2615–2637, November 2009.
Download: [pdf] [ps.gz] 

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.
Download: [pdf] [ps.gz] 

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.
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, and Alexander J. Smola. Kernel Extrapolation. Neurocomputing, 69(7-9):721–729, 2006.
Download: [pdf] [ps.gz] 

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.
Download: [pdf] [ps.gz] 

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.
Download: (unavailable)

S. V. N. Vishwanathan and M. N. Murty. Kohonen's SOM with Cache. Pattern Recognition, 33(11):1927–1929, November 2000.
Download: [pdf] [ps.gz] 

Top Conferences

Amr Ahmed, Choon Hui Teo, S. V. N. Vishwanathan, and Alex Smola. Fair and Balanced: Learning to present news stories. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 333–342, Seattle,Washington, February 2012. (75 out of 362, 20.7% acceptance rate.)
Download: [pdf] [ps.gz] 

Vasil Denchev, Nan Ding, S. V. N. Vishwanathan, and Hartmut Neven. Robust Classification with Adiabatic Quantum Optimization. In Proceedings of the International Conference on Machine Learning, Edinburgh, Scotland, June 2012. (242 out of 890, 27.1% acceptance rate)
Download: [pdf] [ps.gz] 

Asheesh Jain, S. V. N. Vishwanathan, and Manik Varma. Spectral Projected Gradient Descent for Efficient and Large Scale Generalized Multiple Kernel Learning. In Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 750–758, 2012. (163 out of 755, 21.5% acceptance rate)
Download: [pdf] [ps.gz] 

Shin Matsushima, S. V. N. Vishwanathan, and Alex Smola. Linear Support Vector Machines via Dual Cached Loops. In Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 177–185, 2012. (163 out of 755, 21.5% acceptance rate)
Download: [pdf] [ps.gz] 

Hyokun Yun and S. V. N. Vishwanathan. Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs. In Proceedings of International Workshop on Artificial Intelligence and Statistics, pp. 1389–1397, April 2012. (134 out of 400, 33.5% acceptance rate.)
Download: [pdf] [ps.gz] 

William Benjamin, Andrew Wood Polk, S. V. N. Vishwanathan, and Karthik Ramani. Heat Walk:Robust Salient Segmentation of Non-rigid Shapes. In Proceedings of the 19th Pacific Conference on Computer Graphics and Applications, Eurographics Association, Taiwan, September 2011. (27 out of 167, 16% acceptance rate)
Download: [pdf] [ps.gz] 

Nan Ding, S. V. N. Vishwanathan, and Alan Qi. $t$-divergence Based Approximate Inference. In Advances in Neural Information Processing Systems 24, pp. 1494–1502, 2011. (305 out of 1400, 21.8% acceptance rate)
Download: [pdf] [ps.gz] 

Yi Fang, S. V. N. Vishwanathan, Mengtian Sun, and Karthik Ramani. sLLE: Spherical Locally Linear Embedding with Applications to Tomography. In Proceedings of the IEEE Conf. Computer Vision and Pattern Recognition, pp. 1129–1136, Colorado Springs (USA), June 2011. (438 out of 1677, 26.4% acceptance rate)
Download: [pdf] [ps.gz] 

Ankan Saha, S. V. N. Vishwanathan, and Xinhua Zhang. New Approximation Algorithms for Minimum Enclosing Convex Shapes. In ACM-SIAM Syposium on Discrete Algorithms (SODA), pp. 1146–1160, 2011. (136 out of 454, 30% acceptance rate)
Download: [pdf] [ps.gz] 

Xinhua Zhang, Ankan Saha, and S. V. N. Vishwanathan. Smoothing Multivariate Performance Measures. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pp. 814–821, Barcelona, Spain, July 2011. (96 out of 285 34% acceptance rate)
Download: [pdf] [ps.gz] 

Xinhua Zhang, Ankan Saha, and S. V. N. Vishwanathan. Accelerated Training of Max-Margin Markov Networks with Kernels. In Proc. Intl. Conf. Algorithmic Learning Theory, pp. 292–307, Springer-Verlag, Espoo, Finland, October 2011.
Download: [pdf] [ps.gz] 

Nan Ding and S. V. N. Vishwanathan. t-logistic regression. In Advances in Neural Information Processing Systems 23, pp. 514–522, 2010. (293 out of 1219, 24% acceptance rate)
Download: [pdf] [ps.gz] 

Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, and Manik Varma. Large Scale Max-Margin Multi-Label Classification with Prior Knowledge about Densely Correlated Labels. In Proceedings of the International Conference on Machine Learning, 2010. (152 out of 594, 25.6% acceptance rate)
Download: [pdf] [ps.gz] 

Novi Quadrianto, Alex Smola, Tiberio Caetano, S. V. N. Vishwanathan, and James Petterson. Multitask Learning without Label Correspondences. In Advances in Neural Information Processing Systems 23, pp. 1957–1965, 2010. (293 out of 1219, 24% acceptance rate)
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan, Zhaonan Sun, Nawanol Theera-Ampornpunt, and Manik Varma. Multiple Kernel Learning and the SMO Algorithm. In Advances in Neural Information Processing Systems 23, pp. 2361–2369, 2010. Poster spotlight. (73 out of 1219, 6% acceptance rate)
Download: [pdf] [ps.gz] 

Xinhua Zhang, Ankan Saha, and S. V. N. Vishwanathan. Lower Bounds on Rate of Convergence of Cutting Plane Methods. In Advances in Neural Information Processing Systems 23, pp. 2541–2549, 2010. (293 out of 1219, 24% acceptance rate)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

Li Cheng, S. V. N. Vishwanathan, and Xinhua Zhang. Consistent Image Analogies using Semi-supervised Learning. In Proceedings of the IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society, Anchorage, Alaska (USA), June 2008. (508 out of 1593, 32% acceptance rate)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

Jin Yu, S. V. N. Vishwanathan, Simon Günter, and Nicol N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization. In Proceedings of the International Conference on Machine Learning, Helsinki, Finland, July 2008. (155 out of 583, 26.5% acceptance rate)
Download: [pdf] [ps.gz] 

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.
Download: [pdf] [ps.gz] 

Li Cheng and S. V. N. Vishwanathan. Learning to Compress Images and Video. In Proceedings of the International Conference on Machine Learning, pp. 161–168, June 2007. (152 out of 522, 29% acceptance rate)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

Xinhua Zhang, Douglas Aberdeen, and S. V. N. Vishwanathan. Conditional Random Fields for Multi-Agent Reinforcement Learning. In Proceedings of the International Conference on Machine Learning, pp. 1143–1150, June 2007. best student paper award, (152 out of 522, 29% acceptance rate)
Download: [pdf] [ps.gz] 

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.
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

Choon Hui Teo and S. V. N. Vishwanathan. Fast and space efficient string kernels using suffix arrays. In Proceedings of the International Conference on Machine Learning, pp. 929–936, ACM Press, New York, NY, USA, 2006. (140 out of 700, 20% acceptance rate)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark Schmidt, and Kevin Murphy. Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods. In Proceedings of the International Conference on Machine Learning, pp. 969 – 976, ACM Press, New York, NY, USA, 2006. (140 out of 700, 20% acceptance rate)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

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)
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan, Alexander J. Smola, and M. N. Murty. SimpleSVM. In Proceedings of the International Conference on Machine Learning, AAAI press, Washington DC, 2003. (119 out of 371, 32% acceptance rate)
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan and M. N. Murty. Geometric SVM: A fast and intuitive SVM algorithm. In Proc. Intl. Conf. Pattern Recognition, 2002.
Download: [pdf] [ps.gz] 

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.
Download: (unavailable)

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.
Download: [pdf] [ps.gz] 

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.
Download: (unavailable)

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.
Download: (unavailable)

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.
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan and M. N. Murty. Jigsawing: A Method to Generate Virtual Examples in OCR Data. In Hybrid Intelligent Systems, 2002.
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan and M. N. Murty. SSVM: A Simple SVM Algorithm. In Proc. Intl. Joint Conf. on Neural Networks, IEEE Press, 2002.
Download: [pdf] [ps.gz] 

S. V. N. Vishwanathan and M. N. Murty. Use of MPSVM for Data Set Reduction. In Hybrid Information Systems, Physica Verlag, Heidelberg, 2001.
Download: [pdf] [ps.gz] 

Unspecified

Feng Yan, Shreyas Sundaram, S. V. N. Vishwanathan, and Yuan Qi. Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties. IEEE Transactions on Knowledge and Data Engineering, 2012. Accepted
Download: [pdf] [ps.gz] 


Generated by bib2html.pl (written by Patrick Riley) on Sun Aug 25, 2013 15:14:26