P Balamurugan

Post-doctoral Researcher
STatistics and Applications (STA) Group
Telecom-ParisTech, Paris, France.
email: palaniappan[at]telecom-paristech.fr

Post-doctoral Research

I am a post-doctoral researcher in STatistics and Applications (STA) Group, Telecom-ParisTech, Paris, mentored by Professor Stéphan Clémençon and Professor Florence d’Alché-Buc.

Previously, I was a post doctoral researcher at SIERRA Project Team, INRIA-ENS mentored by Professor Francis Bach.

Research Interests

My primary research theme is to develop efficient optimization methods and algorithms for various machine learning, data mining problems. This involves investigation of both the theoretical and practical aspects of optimization methodologies. The interplay between statistical, learning theoretic, algorithmic and probabilistic aspects of machine learning models also fascinate me. I am also interested in looking into applications of machine learning and data mining in relatively unexplored fields.

Recent Technical Report

Publications

2016

  • Stochastic Variance Reduction Methods for Saddle-Point Problems.

    P. Balamurugan, Francis Bach.
    In Advances in Neural Information Processing Systems (NIPS), 2016. [code]
  • Gaussian Process Pseudo-Likelihood Models for Sequence Labeling.

    P. K. Srijith, P. Balamurugan, Shirish Shevade.
    In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), 2016. 
  • ADMM for Training Sparse Structural SVMs with augmented L1 regularizers.

    P. Balamurugan, Anusha Posinasetty, Shirish Shevade.
    In SIAM International Conference on Data Mining (SDM), 2016. [pdf] [supplement pdf] [code]

2015

2014

  • Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs.

    P. Balamurugan, Shirish Shevade, T. Ravindra Babu.
    In Knowledge and Information Systems. Vol. 38(3), pages: 599-621. March, 2014.

2013

  • Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression.

    P. Balamurugan.
    In IEEE International Conference on Data Mining (ICDM), 2013. Acceptance Rate: 19.65% (159/809) [pdf]

  • Optimizing F-Measure With Non-Convex Loss and Sparse Linear Classifiers.

    Punya Murthy Chinta, P. Balamurugan, Shirish Shevade, M. Narasimha Murty.
    In International Joint Conference on Neural Networks (IJCNN), 2013. [pdf]

2012

  • Sequential Alternating Proximal Method for Scalable Sparse Structural SVMs.

    P. Balamurugan, Shirish Shevade, T. Ravindra Babu.
    (Full Paper) In IEEE International Conference on Data Mining (ICDM), 2012. Acceptance Rate for full papers: 10.71% (81/756). [pdf]
    (One of the Best Papers invited for KAIS journal). Code for a fast version of sequential alternating proximal method along with other training methods available at [code link]   
  • Efficient Algorithms for Linear Summed Error Structural SVMs.

    P. Balamurugan, Shirish Shevade, T. Ravindra Babu.
    In International Joint Conference on Neural Networks (IJCNN), 2012. [pdf]

2011

Workshop Papers

  • Robust Discriminative Clustering with Sparse Regularizers.

    Nicolas Flammarion, P. Balamurugan, Francis Bach.
    In NIPS workshop on Feature Extraction: Modern Questions and Challenges, 2015.
  • Efficient Variational Inference for Gaussian Process Structured Prediction.

    P. K. Srijith, P. Balamurugan, Shirish Shevade.
    In NIPS workshop on Advances in Variational Inference, 2014.

Technical Reports and Preprints

Education Details

Work Experience

  • Research Intern at IBM India Research Lab, Bangalore, during May-August, 2013. Mentor: Dr. Vikas C. Raykar.

  • Project Assistant under Dr. Shirish Shevade during August 2009 - December 2009. Worked on "Efficient Algorithms for Structured Prediction".

  • Assistant Systems Engineer at Tata Consultancy Services during 2004 - 2007.

Teaching Assistance

M.E. Thesis

  • L1-Norm Structural Classification SVMs.

Awards

  • Recipient of the Alumni Medal from Computer Science and Automation Department, Indian Institute of Science, for the Best PhD Thesis in 2015.

  • A co-recipient of IBM Best PhD Thesis Award among theses from Computer Science and Automation Department, Indian Institute of Science, for the year 2015.

  • IBM PhD Fellowship awards for the years 2012 and 2013.

  • Travel awards from IBM, Infosys India, Indian Institute of Science (Sarukkai Jagannathan award) and IEEE ICDM.

Review Responsibility

  • A reviewer for NIPS 2016, Neuro Computing Journal, Neural Networks Journal, Science China Mathematics Journal, and Sadhana Academy Proceedings in Engineering Sciences.

Other Activities