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
https://research.aalto.fi/...

Contact information

Areas of expertise

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

Honors and awards

Outstanding Reviewer Award ACL 2021 Conference

Award or honor granted for a specific work Computer Science Professors Jul 2021

Research groups

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

Publications

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification

Erik Schultheis, Rohit Babbar, Marek Wydmuch, Krzysztof Dembczynski 2022 Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification

Siddhant Kharbanda, Atmadeep Banerjee, Akash Palrecha, Devaansh Gupta, Rohit Babbar 2021

Adversarial Examples for Extreme Multilabel Text Classification

Mohammadreza Mohammadnia Qaraei, Rohit Babbar 2021

Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels

Mohammadreza Mohammadnia Qaraei, Erik Schultheis, Priyanshu Gupta, Rohit Babbar 2021 The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021

Propensity-scored Probabilistic Label Trees

Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar, Krzysztof Dembczynski 2021 SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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

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

Data scarcity, robustness and extreme multi-label classification

Rohit Babbar, Bernhard Schölkopf 2019 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

DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

Rohit Babbar, Bernhard Schölkopf 2017 WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

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 Proceedings of the 2016 SIAM International Conference on Data Mining (SDM)

Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data

Georgios Balikas, Ioannis Partalas, Eric Gaussier, Rohit Babbar, Massih-Reza Amini 2015 Advances in Intelligent Data Analysis XIV

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, Eric Gaussier, Massi-reza Amini 2014 SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

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

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

On Flat versus Hierarchical Classification in Large-Scale Taxonomies

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini 2013 NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems

On Empirical Tradeoffs in Large Scale Hierarchical Classification

Rohit Babbar, Ioannis Partalas, Eric Gaussier, Cecile Amblard 2012 CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Adaptive Classifier Selection in Large-scale Hierarchical Classification

Ioannis Partalas, Rohit Babbar, Eric Gaussier, Cecile Amblard 2012 Neural Information Processing