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Rohit Babbar
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DiSMEC : Distributed Sparse Machines for Extreme Multi-label Classification
R Babbar, B Schölkopf
Proceedings of the Tenth ACM International Conference on Web Search and Data …, 2017
2972017
Distributed inference acceleration with adaptive DNN partitioning and offloading
T Mohammed, C Joe-Wong, R Babbar, M Di Francesco
IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 854-863, 2020
1642020
Bonsai: diverse and shallow trees for extreme multi-label classification
S Khandagale, H Xiao, R Babbar
Machine Learning 109 (11), 2099-2119, 2020
1462020
Data scarcity, robustness and extreme multi-label classification
R Babbar, B Schölkopf
Machine Learning 108 (8), 1329-1351, 2019
1442019
On flat versus hierarchical classification in large-scale taxonomies
R Babbar, I Partalas, E Gaussier, MR Amini
27th Annual Conference on Neural Information Processing Systems (NIPS 26 …, 2013
972013
Learning taxonomy adaptation in large-scale classification
R Babbar, I Partalas, E Gaussier, MR Amini, C Amblard
Journal of Machine Learning Research 17 (98), 1-37, 2016
392016
Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text
R Babbar, N Singh
Proceedings of the fourth workshop on Analytics for noisy unstructured text …, 2010
392010
Extreme classification (dagstuhl seminar 18291)
S Bengio, K Dembczynski, T Joachims, M Kloft, M Varma
Schloss-Dagstuhl-Leibniz Zentrum für Informatik, 2019
342019
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels
M Qaraei, E Schultheis, P Gupta, R Babbar
Proceedings of the Web Conference 2021, 3711-3720, 2021
27*2021
On power law distributions in large-scale taxonomies
R Babbar, C Metzig, I Partalas, E Gaussier, MR Amini
ACM SIGKDD explorations newsletter 16 (1), 47-56, 2014
272014
Adversarial extreme multi-label classification
R Babbar, B Schölkopf
arXiv preprint arXiv:1803.01570, 2018
222018
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
E Schultheis, M Wydmuch, R Babbar, K Dembczynski
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
202022
Prediction of glucose tolerance without an oral glucose tolerance test
R Babbar, M Heni, A Peter, M Hrabě de Angelis, HU Häring, A Fritsche, ...
Frontiers in endocrinology 9, 82, 2018
182018
Maximum-margin framework for training data synchronization in large-scale hierarchical classification
R Babbar, I Partalas, E Gaussier, MR Amini
Neural Information Processing: 20th International Conference, ICONIP 2013 …, 2013
162013
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
S Kharbanda, A Banerjee, E Schultheis, R Babbar
Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
152022
Speeding-up one-versus-all training for extreme classification via mean-separating initialization
E Schultheis, R Babbar
Machine Learning 111 (11), 3953-3976, 2022
13*2022
Explainable publication year prediction of eighteenth century texts with the BERT model
I Rastas, YC Ryan, ILI Tiihonen, M Qaraei, L Repo, R Babbar, E Mäkelä, ...
Proceedings of the 3rd Workshop on Computational Approaches to Historical …, 2022
122022
Efficient model selection for regularized classification by exploiting unlabeled data
G Balikas, I Partalas, E Gaussier, R Babbar, MR Amini
Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015
122015
TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification
R Babbar, K Muandet, B Schölkopf
SIAM International Conference on Data Mining, 234-242, 2016
102016
Re-ranking approach to classification in large-scale power-law distributed category systems
R Babbar, I Partalas, E Gaussier, MR Amini
Proceedings of the 37th international ACM SIGIR conference on Research …, 2014
92014
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Articles 1–20