Rohit Babbar


Hello, I work in the Empirical Inference group headed by Prof. Bernhard Schölkopf , at Max-Planck Institute for Intelligent Systems, Tübingen, Germany. 

Research interests : Machine Learning for large-scale problems, Extreme Classification, Deep Learning, and Optimization... 

Before this, I finished my PhD in October 2014 from LIG lab of University of Grenoble Alpes,  France, where I was advised by Prof. Eric Gaussier and Prof. Massih-Reza Amini under the Class-Y project funded by the French National Research Agency (ANR). 


Extreme Classification Repository  - Maintained by Manik Varma, consists of Datasets and (links to) code for most recent methods for extreme classification











Publications:

    2017
  • DiSMEC : Distributed Sparse Machines for Extreme Multi-label Classificationpdf 
    Rohit Babbar and  Bernhard Schölkopf
    ACM International Conference on Web Search and Data Mining (
    WSDM) 2017, Cambridge;
    Also accepted for NIPS 2016 Extreme Classification Workshop (pls email me if you want to try DiSMEC code)
   2016
  • TerseSVM : A scalable approach for learning compact models in Large-scale classification, pdf
    Rohit Babbar, Krikamol Muandet, and  Bernhard Schölkopf
    SIAM International Conference on Data Mining (
    SDM 2016), Miami, (pls email me for the code)

  • Learning Taxonomy Adaptation in Large-scale Classification
    Rohit Babbar, Ioannis Partalas, Eric Gaussier and Massih-reza Amini
    Journal of Machine Learning Research , (JMLR 2016)
   2015
  • Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data 
    Georgios Balikas, Ioannis Partalas, Eric Gaussier, Rohit Babbar and Massih-Reza Amini
    International Symposium on Intelligent Data Analysis, (IDA 2015),  Saint-Etienne, France
   2014
  • Re-ranking Approach to Classification in Large-scale Power-law Distributed Category Systems, pdf
    Rohit Babbar, Ioannis Partalas, Eric Gaussier and Massih-reza Amini
    ACM SIGIR Conference , (
    SIGIR 2014), Gold Coast, Australia, 
  • On Power Law Distributions in Large-scale Taxonomies", pdf
    Rohit BabbarCornelia Metzig,  Ioannis Partalas, Eric Gaussier and Massih-reza Amini
    SIGKDD Explorations Journal, Special Issue on Big Data
  2013
  • On Flat versus Hierarchical Classification in Large-Scale Taxonomies", pdf  
    Rohit Babbar, Ioannis Partalas, Eric Gaussier and Massih-reza Amini
    Neural Information Processing Systems, (
    NIPS 2013), Lake Tahoe, Neveda, USA

  • Maximum-margin Framework for Training Data Synchronization in Large-scale Hierarchical Classification, pdf, 
    Rohit Babbar, Ioannis Partalas, Eric Gaussier and Massih-reza Amini 
    Intl. Conference on Neural Information Processing, (
    ICONIP 2013), Daegu, Korea

  • Comparative Classifier Evaluation for Web-scale Taxonomies using Power Law, 
    Rohit Babbar, Ioannis Partalas, Cornelia Metzig, Eric Gaussier and Massih-reza Amini
    European Semantic Web Conference (
    ESWC 2013), Montpiller, France.
   2012
  • On Empirical Tradeoffs in Large Scale Hierarchical Classification
    Rohit Babbar, Ioannis Partalas, Eric Gaussier, Cecile Amblard
    ACM Intl. Conference on Information and Knowledge Management, (
    CIKM 2012), Maui, Hawaii.

  • Adaptive Classifier Selection in Large-scale Hierarchical Classification
    Ioannis Partalas, Rohit Babbar, Eric Gaussier, Cecile Amblard
    Intl. Conference on Neural Information Processing, (
    ICONIP 2012), Doha, Qatar.

  • Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text
    Rohit Babbar, Nidhi Singh
    Conference on Information and Knowledge Management, (CIKM 2010) workshop on Analytics for Noisy Unstructured Text, Toronto, Canada


Educational Background :
  • Doctoral Degree in Computer Science from University of Grenoble Alpes, defended in October 2014.
  • Master in Computer Science, 2009-2011, from Chennai Mathematical Institute, India. Did my master thesis(Jan-June, 2011) under supervision of Dr. Manik Varma and Prof. Madhavan Mukund to study the regularization path for p-norm Multiple Kernel Learning

  • B. Tech. from NIT Kurukshetra, in ECE (2000-2004), worked in IBM India from 2004 to 2009.

Invited Talks :

  • Magnet Team, INRIA, Lille, January, 2017

  • ABC Team, LORIA, Nancy, November, 2016

  • Big Data for Material Sciences Workhsop, Ringberg Castle, July 2016

  • Data Analytics Lab, ETH Zurich, November 2015

  • Bosch Corporate Research Center, Renningen, October 2015

  • Magnet Team, INRIA, Lille, December 2014

Other Talks/Poster Presentations :
  • WSDM 2017, Cambridge

  • NIPS XMC workshop 2016, Barcelona

  • SDM 2016, Miami

  • SIGIR 2014, Gold Coast

  • NIPS 2013, Lake Tahoe

  • ICONIP 2013, Daegu

  • CIKM 2012, Hawaii

Other Stuff

- Long ago, finished Grenoble-Vizille Semi-marathon


Locations of Site Visitors
Ċ
dismec.pdf
(406k)
Rohit Babbar,
Sep 8, 2016, 12:31 PM
Ċ
Rohit Babbar,
Jul 9, 2014, 3:13 PM
Ċ
siam.pdf
(256k)
Rohit Babbar,
Sep 8, 2016, 12:10 PM
Ċ
Rohit Babbar,
Jul 9, 2014, 3:05 PM
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