Rohit Babbar

Rohit Babbar

Assistant professor
Assistant professor
T313 Dept. Computer Science

I am an Assistant Professor at the department of Computer Science, Aalto University in Finland. Along with my research group, we work on problems in large scale machine learning particularly encountered in extreme classification with large output spaces, and robustness

Full researcher profile

Areas of expertise

large scale learning, extreme multi-label classification, deep learning, sequential data, robustness

Research groups

  • Professorship Babbar Rohit, Assistant Professor
  • Computer Science Professors, Assistant Professor
  • Computer Science - Artificial Intelligence and Machine Learning (AIML), Assistant Professor


Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification

Sujay Khandagale, Han Xiao, Rohit Babbar 2020 Machine Learning

Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification

Mohammadreza Mohammadnia Qaraei, Sujay Khandagale, Rohit Babbar 2020 ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading

Thaha Mohammed, Carlee Joe-Wong, Rohit Babbar, Mario Di Francesco 2020 INFOCOM 2020 - IEEE Conference on Computer Communications

Neural Architecture Search for Extreme Multi-label Text Classification

Loïc Pauletto, Massih-Reza Amini, Rohit Babbar, Nicolas Winckler 2020 International Conference on Neural Information Processing

Unbiased Loss Functions for Extreme Classification With Missing Labels

Erik Schultheis, Mohammadreza Mohammadnia Qaraei, Priyanshu Gupta, Rohit Babbar 2020

Data scarcity, robustness and extreme multi-label classification

Rohit Babbar, Bernhard Schölkopf 2019 Machine Learning

A Simple and Effective Scheme for Data Pre-processing in Extreme Classification

Sujay Khandagale, Rohit Babbar 2019 ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Adversarial Extreme Multi-label Classification

Rohit Babbar, Bernhard Schoelkopf 2018

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 2018 Frontiers in Endocrinology

Extreme Multi-label Classification for Information Retrieval

Krzysztof Dembczynski, Rohit Babbar 2018 Advances in Information Retrieval

Learning Taxonomy Adaptation in Large-scale Classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini, Cecile Amblard 2016 Journal of Machine Learning Research

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

Rohit Babbar, Krikamol Muandet, Bernhard Schölkopf 2016

Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data

Georgios Balikas, Ioannis Partalas, Eric Gaussier, Rohit Babbar, Massih-Reza Amini 2015

On Power Law Distributions in Large-scale Taxonomies

Rohit Babbar, Ioannis Partalas, Cornelia Metzig, Eric Gaussier, Massih-Reza Amini 2014 SIGKDD Explorations

Re-ranking Approach to Classification in Large-scale Power-law Distributed Category Systems

Rohit Babbar, Ioannis Partalas, Massi-reza Amini, Eric Gaussier 2014

Maximum-margin Framework for Training Data Synchronization in Large-scale Hierarchical Classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini 2013

On Flat versus Hierarchical Classification in Large-Scale Taxonomies

Rohit Babbar, Ioannis Partalas, Massih-Reza Amini, Eric Gaussier 2013

On Empirical Tradeoffs in Large Scale Hierarchical Classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Cecile Amblard 2012

Adaptive Classifier Selection in Large-scale Hierarchical Classification

Ioannis Partalas, Rohit Babbar, Eric Gaussier, Cecile Amblard 2012