Hello, I am an Assistant Professor in the department of Computer Science, Aalto University at Helsinki, Finland. In my team, we work on large-scale machine learning problems particularly in Extreme Classification and learning with imperfect supervision. I am looking for research assistants to work on the problems in these areas. If interested, please contact at firstname.lastname@aalto.fi

Before this, I was a post-doc at Max-Planck Institute for Intelligent Systems, Tuebingen, Germany in the group of Prof. Bernhard Schölkopf. I finished my PhD from University of Grenoble, France where I was advised by Prof. Eric Gaussier and Prof. Massih-Reza Amini

Recent pre-prints

  • Unbiased Loss Functions for Multilabel Classification with Missing Labels, 2021, (arXiv pre-print)
    Erik Schultheis,
    Rohit Babbar

  • Embedding Convolutions for Short Text Extreme Classification with Millions of Labels, 2021, (arXiv pre-print)
    Siddhant Kharbanda
    , Atmadeep Banerjee, Akash Palrecha, Rohit Babbar

  • Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization, 2021, (arxiv pre-print)
    Erik Schultheis, Rohit Babbar

Selected Publications (All publications on google scholar)

  • Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels (pdf, video, code)
    Mohammadreza Qaraei, Erik Schultheis, Priyanshu Gupta, Rohit Babbar
    30th ACM International World Wide Web Conference, The WebConf (WWW) 2021

  • Propensity-scored Probabilistic Label Trees pdf
    Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar and Krzysztof Dembczyński
    44th International ACM SIGIR Conference on Research and Development in Information Retrieval (
    SIGIR 2021)

  • Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading pdf
    Mohammed Thaha, Carlee Joe-Wong, Rohit Babbar, Mario Di Francesco
    20th IEEE International Conference on Computer Communications,
    Infocom 2020

  • Data Scarcity, Robustness and Extreme Multi-label Classification pdf Code, Datasets
    Rohit Babbar and Bernhard Schölkopf
    Machine Learning Journal and European Conference on Machine Learning (ECML) 2019

  • DiSMEC : Distributed Sparse Machines for Extreme Multi-label Classification, pdf Code Dataset
    Rohit Babbar and Bernhard Schölkopf
    ACM International Conference on Web Search and Data Mining (
    WSDM) 2017, Cambridge

  • 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

  • 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

Teaching

  • Kernel Methods in Machine Learning (5 ECTS, Spring 2019, 2020, 2021)

  • Introduction to Data Science (Bachelor level, 5 ECTS, Fall 2019)

  • Supervised Learning with Large Label Sets ( 3 ECTS, Spring 2018)

Reviewing activities :

  • 2019 - AAAI, ICLR, AISTATS, ICML, NIPS, IJCAI, JMLR, MLJ

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

  • 2017 - NIPS, ICML, ICLR, AISTATS

Academic Trail :


Other Stuff

- Long ago, finished Grenoble-Vizille Semi-marathon