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Rohit Babbar
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2020 – today
- 2024
- [j7]Mohammadreza Qaraei, Rohit Babbar:
Meta-classifier free negative sampling for extreme multilabel classification. Mach. Learn. 113(2): 675-697 (2024) - [c27]Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski:
Consistent algorithms for multi-label classification with macro-at-k metrics. ICLR 2024 - [c26]Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski:
A General Online Algorithm for Optimizing Complex Performance Metrics. ICML 2024 - [c25]Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar:
Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features. KDD 2024: 1360-1371 - [i18]Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski:
Consistent algorithms for multi-label classification with macro-at-k metrics. CoRR abs/2401.16594 (2024) - [i17]Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Cho-Jui Hsieh, Rohit Babbar:
UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification. CoRR abs/2405.03714 (2024) - [i16]Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar:
Learning label-label correlations in Extreme Multi-label Classification via Label Features. CoRR abs/2405.04545 (2024) - [i15]Jinbin Zhang, Nasib Ullah, Rohit Babbar:
Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models. CoRR abs/2406.09288 (2024) - [i14]Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski:
A General Online Algorithm for Optimizing Complex Performance Metrics. CoRR abs/2406.14743 (2024) - 2023
- [c24]Aatu Liimatta, Eetu Mäkelä, Filip Ginter, Iiro Rastas, Iiro Tiihonen, Jinbin Zhang, Lidia Pivovarova, Mikko Tolonen, Milja K, Rohit Babbar, Ruilin Wang, Tanja Säily, Yann Ciarán Ryan:
Using ECCO-BERT and the Historical Thesaurus of English to Explore Concepts and Agency in Historical Writing Interpreting the Eighteenth-century Luxury Debate. DH 2023 - [c23]Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski:
Generalized test utilities for long-tail performance in extreme multi-label classification. NeurIPS 2023 - [c22]Erik Schultheis, Rohit Babbar:
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU. ECML/PKDD (3) 2023: 689-704 - [c21]Siddhant Kharbanda, Atmadeep Banerjee, Devaansh Gupta, Akash Palrecha, Rohit Babbar:
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification. SIGIR 2023: 760-769 - [i13]Erik Schultheis, Rohit Babbar:
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 500k Labels on a Single Commodity GPU. CoRR abs/2306.03725 (2023) - [i12]Erik Schultheis, Marek Wydmuch, Wojciech Kotlowski, Rohit Babbar, Krzysztof Dembczynski:
Generalized test utilities for long-tail performance in extreme multi-label classification. CoRR abs/2311.05081 (2023) - 2022
- [j6]Erik Schultheis, Rohit Babbar:
Speeding-up one-versus-all training for extreme classification via mean-separating initialization. Mach. Learn. 111(11): 3953-3976 (2022) - [j5]Mohammadreza Qaraei, Rohit Babbar:
Adversarial examples for extreme multilabel text classification. Mach. Learn. 111(12): 4539-4563 (2022) - [c20]Iiro Rastas, Yann Ciarán Ryan, Iiro Tiihonen, Mohammadreza Qaraei, Liina Repo, Rohit Babbar, Eetu Mäkelä, Mikko Tolonen, Filip Ginter:
Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model. LChange@ACL 2022: 68-77 - [c19]Jinbin Zhang, Yann Ciarán Ryan, Iiro Rastas, Filip Ginter, Mikko Tolonen, Rohit Babbar:
Detecting Sequential Genre Change in Eighteenth-Century Texts. CHR 2022: 243-255 - [c18]Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski:
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. KDD 2022: 1547-1557 - [c17]Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar:
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. NeurIPS 2022 - [p1]Erik Schultheis, Rohit Babbar:
Extreme Multicore Classification. Mach. Learn. under Resour. Constraints Vol. 1 (1) 2022: 249-285 - [i11]Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski:
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. CoRR abs/2207.13186 (2022) - [i10]Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar:
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. CoRR abs/2211.00640 (2022) - 2021
- [c16]Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar, Krzysztof Dembczynski:
Propensity-scored Probabilistic Label Trees. SIGIR 2021: 2252-2256 - [c15]Mohammadreza Qaraei, Erik Schultheis, Priyanshu Gupta, Rohit Babbar:
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels. WWW 2021: 3711-3720 - [i9]Siddhant Kharbanda, Atmadeep Banerjee, Akash Palrecha, Rohit Babbar:
Embedding Convolutions for Short Text Extreme Classification with Millions of Labels. CoRR abs/2109.07319 (2021) - [i8]Erik Schultheis, Rohit Babbar:
Unbiased Loss Functions for Multilabel Classification with Missing Labels. CoRR abs/2109.11282 (2021) - [i7]Erik Schultheis, Rohit Babbar:
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization. CoRR abs/2109.13122 (2021) - [i6]Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar, Krzysztof Dembczynski:
Propensity-scored Probabilistic Label Trees. CoRR abs/2110.10803 (2021) - [i5]Mohammadreza Qaraei, Rohit Babbar:
Adversarial Examples for Extreme Multilabel Text Classification. CoRR abs/2112.07512 (2021) - 2020
- [j4]Sujay Khandagale, Han Xiao, Rohit Babbar:
Bonsai: diverse and shallow trees for extreme multi-label classification. Mach. Learn. 109(11): 2099-2119 (2020) - [c14]Mohammadreza Qaraei, Sujay Khandagale, Rohit Babbar:
Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification. ESANN 2020: 223-228 - [c13]Loïc Pauletto, Massih-Reza Amini, Rohit Babbar, Nicolas Winckler:
Neural Architecture Search for Extreme Multi-label Text Classification. ICONIP (3) 2020: 282-293 - [c12]Thaha Mohammed, Carlee Joe-Wong, Rohit Babbar, Mario Di Francesco:
Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading. INFOCOM 2020: 854-863 - [i4]Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta, Rohit Babbar:
Unbiased Loss Functions for Extreme Classification With Missing Labels. CoRR abs/2007.00237 (2020)
2010 – 2019
- 2019
- [j3]Rohit Babbar, Bernhard Schölkopf:
Data scarcity, robustness and extreme multi-label classification. Mach. Learn. 108(8-9): 1329-1351 (2019) - [c11]Sujay Khandagale, Rohit Babbar:
A Simple and Effective Scheme for Data Pre-processing in Extreme Classification. ESANN 2019 - [i3]Sujay Khandagale, Han Xiao, Rohit Babbar:
Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification. CoRR abs/1904.08249 (2019) - 2018
- [i2]Rohit Babbar, Bernhard Schölkopf:
Adversarial Extreme Multi-label Classification. CoRR abs/1803.01570 (2018) - 2017
- [c10]Rohit Babbar, Bernhard Schölkopf:
DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. WSDM 2017: 721-729 - 2016
- [j2]Rohit Babbar, Ioannis Partalas, Éric Gaussier, Massih-Reza Amini, Cécile Amblard:
Learning Taxonomy Adaptation in Large-scale Classification. J. Mach. Learn. Res. 17: 98:1-98:37 (2016) - [c9]Rohit Babbar, Krikamol Muandet, Bernhard Schölkopf:
TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. SDM 2016: 234-242 - [i1]Rohit Babbar, Bernhard Schölkopf:
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification. CoRR abs/1609.02521 (2016) - 2015
- [c8]Georgios Balikas, Ioannis Partalas, Éric Gaussier, Rohit Babbar, Massih-Reza Amini:
Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data. IDA 2015: 25-36 - 2014
- [b1]Rohit Babbar:
Machine Learning Strategies for Large-scale Taxonomies. (Strategies d'apprentissage pour la classification dans les grandes taxonomies). Grenoble Alpes University, France, 2014 - [j1]Rohit Babbar, Cornelia Metzig, Ioannis Partalas, Éric Gaussier, Massih-Reza Amini:
On power law distributions in large-scale taxonomies. SIGKDD Explor. 16(1): 47-56 (2014) - [c7]Rohit Babbar, Ioannis Partalas, Éric Gaussier, Massih-Reza Amini:
Re-ranking approach to classification in large-scale power-law distributed category systems. SIGIR 2014: 1059-1062 - 2013
- [c6]Rohit Babbar, Ioannis Partalas, Cornelia Metzig, Éric Gaussier, Massih-Reza Amini:
Comparative Classifier Evaluation for Web-Scale Taxonomies Using Power Law. ESWC (Satellite Events) 2013: 310-311 - [c5]Rohit Babbar, Ioannis Partalas, Éric Gaussier, Massih-Reza Amini:
Maximum-Margin Framework for Training Data Synchronization in Large-Scale Hierarchical Classification. ICONIP (1) 2013: 336-343 - [c4]Rohit Babbar, Ioannis Partalas, Éric Gaussier, Massih-Reza Amini:
On Flat versus Hierarchical Classification in Large-Scale Taxonomies. NIPS 2013: 1824-1832 - 2012
- [c3]Rohit Babbar, Ioannis Partalas, Éric Gaussier, Cécile Amblard:
On empirical tradeoffs in large scale hierarchical classification. CIKM 2012: 2299-2302 - [c2]Ioannis Partalas, Rohit Babbar, Éric Gaussier, Cécile Amblard:
Adaptive Classifier Selection in Large-Scale Hierarchical Classification. ICONIP (3) 2012: 612-619 - 2010
- [c1]Rohit Babbar, Nidhi Singh:
Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text. AND 2010: 43-50
Coauthor Index
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last updated on 2024-10-07 22:21 CEST by the dblp team
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