Research Interests

My research interest primarily is in the area of Machine Learning, with focus on Kernel methods. The main theme in most of my works is developing novel optimization formulations and algorithms, along with relevant theoretical gaurantees, that can be applied to diverse machine learning applications. The key tools that I leverage in the context of learning are Optimization and Statistics. I am currently pursuing projects on i) Kernel Embeddings ii) Causality in ML iii) Some text/vision related applications.

Publications

Journals

  1. Pratik Jawapuria, J. SakethaNath and Ganesh Ramakrishnan. Generalized Hierarchical Kernel Learning. Journal of Machine Learning Research, vol. 16, Pg. 617-652, Mar 2015 pdf.
  2. J. Saketha Nath, A. Ben-Tal and C. Bhattacharyya. Robust formulations for clustering-based large-scale classification. Journal of Optimization & Engg., vol. 14(2), Pg. 225-250, June 2013. pdf | code | page1|page2
  3. J. Aflalo, A. Ben-Tal, C. Bhattacharyya, J. Saketha Nath and S. Raman. Variable Sparsity Kernel Learning. Journal of Machine Learning Research, vol. 12, Pg. 565-592, 2011. (web-page, code, pdf)
  4. A. Ben-Tal, S. Bhadra, C. Bhattacharyya and J. Saketha Nath. Chance Constrained Uncertain Classification via Robust Optimization. Mathematical Programming Series B (special issue on Machine Learning), vol. 127(1), Pg. 145-173, 2010. (pdf)
  5. J. Saketha Nath and S. K. Shevade. An efficient clustering scheme using support vector methods. Pattern Recognition, vol. 39(8), Pg. 1473-1480, 2006. pdf | code

Conference Proceedings

  1. Arun Iyer, Saketha Nath J and Sunita Sarawagi. Privacy-preserving Class Ratio Estimation. ACM SIG KDD 2016 (pdf).
  2. Pratik J., Manik Varma and Saketha Nath J. On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection. ICML 2014 (pdf).
  3. Arun Iyer, Saketha Nath J and Sunita Sarawagi. Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection. Accepted in ICML 2014 (pdf).
  4. Ankit Ramteke, Akshat Malu, Pushpak Bhattacharyya and Saketha Nath. Detecting Turnarounds in Sentiment Analysis: Thwarting. ACL Short papers 2013.
  5. Pratik J., and J. Saketha Nath. A Convex Feature Learning Formulation for Latent Task Structure Discovery. ICML-2012, pdf.
  6. Pratik J., J. Saketha Nath and Ganesh R. Efficient Rule Ensemble Learning using Hierarchical Kernels. ICML-2011. pdf | techrep | code.
  7. Pratik J., and J. Saketha Nath. Multi-task Multiple Kernel Learning. SDM2011. Draft. Code.
  8. J. Saketha Nath, G. Dinesh, S. Raman, C. Bhattacharyya, A. Ben-Tal, K. R. Ramakrishnan. On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation. Advances in Neural Information Processing Systems (NIPS), Vancouver, 2009. (pdf)
  9. S. Bhadra, J. Saketha Nath, A. Ben-Tal and C. Bhattacharyya. Interval Data Classification under Partial Information: A Chance-Constraint Approach. In Proceedings of the PAKDD conference, Bangkok, 2009. [Best Paper - Runner Up]. pdf | slides
  10. R. Babaria, J. Saketha Nath, S. Krishnan, Sivaramakrishnan, C. Bhattacharyya and M. N. Murty. Focussed Crawling with Scalable Ordinal Regression Solvers. In Proceedings of the ICML conference, Oregon, 2007. pdf | slides | poster
  11. J. Saketha Nath and C. Bhattacharyya. Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates. In Proceedings of SDM conference, Minneapolis, 2007. pdf
  12. J. Saketha Nath, C. Bhattacharyya and M. N. Murty. Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation. In Proceedings of the SIGKDD conference, Philadelphia, 2006. pdf

Thesis

Professional Activities

Students@IITH

Students@IITB