Rohit Babbar

Assistant Professor, Professorship Babbar R.
Assistant Professor, Department of Computer Science

Phone+358 50 5122646

Aalto SCI Computer Science Konemiehentie 2

Research output per year

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Personal profile

Artistic and research interests

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

Keywords

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

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3 Similar Profiles

5 Conference contribution
5 Article
3 Paper
2 Working paper

Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification
Khandagale, S., Xiao, H. & Babbar, R., 23 Aug 2020, In : Machine Learning.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
35 Downloads (Pure)
Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading
Mohammed, T., Joe-Wong, C., Babbar, R. & Francesco, M. D., Jul 2020, INFOCOM 2020 - IEEE Conference on Computer Communications. IEEE, p. 854-863 10 p. 9155237. (Proceedings - IEEE INFOCOM; vol. 2020-July).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
File
1 Citation (Scopus)

189 Downloads (Pure)
Neural Architecture Search for Extreme Multi-label Text Classification
Pauletto, L., Amini, M-R., Babbar, R. & Winckler, N., 2020, International Conference on Neural Information Processing: ICONIP 2020: Neural Information Processing . p. 282-293 12 p. (Lecture Notes in Computer Science; vol. 12534).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Open Access
Unbiased Loss Functions for Extreme Classification With Missing Labels
Schultheis, E., Mohammadnia Qaraei, M., Gupta, P. & Babbar, R., 1 Jul 2020.
Research output: Working paper › Scientific

File

6 Downloads (Pure)
Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification
Mohammadnia Qaraei, M., Khandagale, S. & Babbar, R., 2020, ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. p. 223-228 6 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

1 Organization of a workshop, panel, session or tutorial
1 Participant of a conference, workshop, session or tutorial

Dagstuhl Seminar
Rohit Babbar (Member)
15 Jul 2018 → 20 Jul 2018
Activity: Participating in or organising an event types › Participant of a conference, workshop, session or tutorial
Tutorial on Extreme Classification for Information Retrieval
Rohit Babbar (Member)
26 Apr 2018
Activity: Participating in or organising an event types › Organization of a workshop, panel, session or tutorial

Artistic and research interests

Keywords

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

Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading

Neural Architecture Search for Extreme Multi-label Text Classification

Unbiased Loss Functions for Extreme Classification With Missing Labels

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

Dagstuhl Seminar

Tutorial on Extreme Classification for Information Retrieval