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
Email
[email protected]
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
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization
Erik Schultheis, Rohit Babbar
2021
Unbiased Loss Functions for Multilabel Classification with Missing Labels
Erik Schultheis, Rohit Babbar
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