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

DiSMEC is a state-of-art classifier for distributed training for datasets with millions of labels. Compared to tree-based methods such as Parabel (which works for vanilla P@k metrics) or PFastreXML (for propensity scored variants), DiSMEC gives much better results on both the metrics with no hyper-parameter tuning. Code can be downloaded here.

News

• Our research is supported by Criteo Faculty Research Grant. Thanks to Criteo!
• Our research is supported by Azure for Research in Data Science grant. Thanks to Microsoft!
• I will be participating in the Dagstuhl seminars on Extreme Classification in July 2018.
• With Krzysztof Dembczyński, I am co-organizing a tutorial on Extreme Classification at ECIR 2018, Grenoble.
• 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.

Revewing activities :

• 2019 - AAAI, ICLR
• 2018 - NIPS, ICML, ICLR, AISTATS, AAAI, JMLR, IEEE PAMI
• 2017 - NIPS, ICML, ICLR, AISTATS

Publications:

2018

• Adversarial Extreme Multi-label Classification pdf

Rohit Babbar and Bernhard Schölkopf

• Prediction of glucose tolerance without an oral glucose tolerance test

Rohit Babbar, Martin Heni, Andreas Peter, Martin Hrabě de Angelis, Hans-Ulrich Häring, Andreas Fritsche, Hubert Preissl, Bernhard Schölkopf, Róbert Wagner

Frontiers in Endocrinology

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

• Learning Taxonomy Adaptation in Large-scale Classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier and Massih-reza Amini

Journal of Machine Learning Research , (JMLR 2016)

• TerseSVM : A scalable approach for learning compact models in Large-scale classification

Rohit Babbar, Krikamol Muandet, and Bernhard Schölkopf

SIAM International Conference on Data Mining (SDM 2016), Miami

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.

Academic Trail :

• Researcher at the Empirical Inference group headed by Prof. Bernhard Schölkopf (2014-2017), at Max-Planck Institute for Intelligent Systems, Tübingen, Germany
• Doctoral Degree in Computer Science from University of Grenoble Alpes, (2011-2014) advised by Prof. Eric Gaussier and Prof. Massih-Reza Amini.
• 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

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