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

We are organizing MPI-ETH Pre-doc Summer School on Learning Systems from July 3 to Friday July 7, 2017 in Zürich, Switzerland for Master's students.

Publications:

2017

DiSMEC : Distributed Sparse Machines for Extreme Multi-label Classification, pdf Code
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

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 Babbar, Cornelia 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.

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

Talks :