Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text. - Rohit Babbar; Nidhi Singh 001 (AND, 2010)

On empirical tradeoffs in large scale hierarchical classification. - Rohit Babbar; Ioannis Partalas; Éric Gaussier; Cécile Amblard (CIKM, 2012)

Adaptive Classifier Selection in Large-Scale Hierarchical Classification. - Ioannis Partalas; Rohit Babbar; Éric Gaussier; Cécile Amblard (ICONIP (3), 2012)

Comparative Classifier Evaluation for Web-Scale Taxonomies Using Power Law. - Rohit Babbar; Ioannis Partalas; Cornelia Metzig; Éric Gaussier; Massih-Reza Amini (ESWC (Satellite Events), 2013)

Maximum-Margin Framework for Training Data Synchronization in Large-Scale Hierarchical Classification. - Rohit Babbar; Ioannis Partalas; Éric Gaussier; Massih-Reza Amini (ICONIP (1), 2013)

On Flat versus Hierarchical Classification in Large-Scale Taxonomies. - Rohit Babbar; Ioannis Partalas; Éric Gaussier; Massih-Reza Amini (NIPS, 2013)

Re-ranking approach to classification in large-scale power-law distributed category systems. - Rohit Babbar; Ioannis Partalas; Éric Gaussier; Massih-Reza Amini (SIGIR, 2014)

On power law distributions in large-scale taxonomies. - Rohit Babbar; Cornelia Metzig; Ioannis Partalas; Éric Gaussier; Massih-Reza Amini (SIGKDD Explor., 2014)

Machine Learning Strategies for Large-scale Taxonomies. (Strategies d'apprentissage pour la classification dans les grandes taxonomies). - Rohit Babbar (2014)

Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data. - Georgios Balikas; Ioannis Partalas; Éric Gaussier; Rohit Babbar; Massih-Reza Amini (IDA, 2015)

TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. - Rohit Babbar; Krikamol Muandet; Bernhard Schölkopf (SDM, 2016)

Learning Taxonomy Adaptation in Large-scale Classification. - Rohit Babbar; Ioannis Partalas; Éric Gaussier; Massih-Reza Amini; Cécile Amblard (J. Mach. Learn. Res., 2016)

DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. - Rohit Babbar; Bernhard Schölkopf (WSDM, 2017)

Adversarial Extreme Multi-label Classification. - Rohit Babbar; Bernhard Schölkopf (CoRR, 2018)

A Simple and Effective Scheme for Data Pre-processing in Extreme Classification. - Sujay Khandagale; Rohit Babbar (ESANN, 2019)

Data scarcity, robustness and extreme multi-label classification. - Rohit Babbar; Bernhard Schölkopf (Mach. Learn., 2019)

Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification. - Mohammadreza Qaraei; Sujay Khandagale; Rohit Babbar (ESANN, 2020)

Neural Architecture Search for Extreme Multi-label Text Classification. - Loïc Pauletto; Massih-Reza Amini; Rohit Babbar; Nicolas Winckler (ICONIP (3), 2020)

Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading. - Thaha Mohammed 001; Carlee Joe-Wong; Rohit Babbar; Mario Di Francesco (INFOCOM, 2020)

Unbiased Loss Functions for Extreme Classification With Missing Labels. - Erik Schultheis; Mohammadreza Qaraei; Priyanshu Gupta; Rohit Babbar (CoRR, 2020)

Bonsai: diverse and shallow trees for extreme multi-label classification. - Sujay Khandagale; Han Xiao; Rohit Babbar (Mach. Learn., 2020)

Propensity-scored Probabilistic Label Trees. - Marek Wydmuch; Kalina Jasinska-Kobus; Rohit Babbar; Krzysztof Dembczynski (SIGIR, 2021)

Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels. - Mohammadreza Qaraei; Erik Schultheis; Priyanshu Gupta; Rohit Babbar (WWW, 2021)

Embedding Convolutions for Short Text Extreme Classification with Millions of Labels. - Siddhant Kharbanda; Atmadeep Banerjee; Akash Palrecha; Rohit Babbar (CoRR, 2021)

Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization. - Erik Schultheis; Rohit Babbar (CoRR, 2021)

Extreme Multicore Classification. - Erik Schultheis; Rohit Babbar (Mach. Learn. under Resour. Constraints Vol. 1 (1), 2022)

Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model. - Iiro Rastas; Yann Ciarán Ryan; Iiro Tiihonen; Mohammadreza Qaraei; Liina Repo; Rohit Babbar; Eetu Mäkelä; Mikko Tolonen; Filip Ginter (LChange@ACL, 2022)

Detecting Sequential Genre Change in Eighteenth-Century Texts. - Jinbin Zhang; Yann Ciarán Ryan; Iiro Rastas; Filip Ginter; Mikko Tolonen; Rohit Babbar (CHR, 2022)

On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. - Erik Schultheis; Marek Wydmuch; Rohit Babbar; Krzysztof Dembczynski (KDD, 2022)

CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. - Siddhant Kharbanda; Atmadeep Banerjee; Erik Schultheis; Rohit Babbar (NeurIPS, 2022)

Adversarial examples for extreme multilabel text classification. - Mohammadreza Qaraei; Rohit Babbar (Mach. Learn., 2022)

Speeding-up one-versus-all training for extreme classification via mean-separating initialization. - Erik Schultheis; Rohit Babbar (Mach. Learn., 2022)

Using ECCO-BERT and the Historical Thesaurus of English to Explore Concepts and Agency in Historical Writing Interpreting the Eighteenth-century Luxury Debate. - Aatu Liimatta; Eetu Mäkelä; Filip Ginter; Iiro Rastas; Iiro Tiihonen; Jinbin Zhang; Lidia Pivovarova; Mikko Tolonen; Milja K; Rohit Babbar; Ruilin Wang; Tanja Säily; et al. (DH, 2023)

Generalized test utilities for long-tail performance in extreme multi-label classification. - Erik Schultheis; Marek Wydmuch; Wojciech Kotlowski; Rohit Babbar; Krzysztof Dembczynski (NeurIPS, 2023)

Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU. - Erik Schultheis; Rohit Babbar (ECML/PKDD (3), 2023)

InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification. - Siddhant Kharbanda; Atmadeep Banerjee; Devaansh Gupta; Akash Palrecha; Rohit Babbar (SIGIR, 2023)

Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 500k Labels on a Single Commodity GPU. - Erik Schultheis; Rohit Babbar (CoRR, 2023)

Consistent algorithms for multi-label classification with macro-at-k metrics. - Erik Schultheis; Wojciech Kotlowski; Marek Wydmuch; Rohit Babbar; Strom Borman; Krzysztof Dembczynski (ICLR, 2024)

A General Online Algorithm for Optimizing Complex Performance Metrics. - Wojciech Kotlowski; Marek Wydmuch; Erik Schultheis; Rohit Babbar; Krzysztof Dembczynski (ICML, 2024)

Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features. - Siddhant Kharbanda; Devaansh Gupta; Erik Schultheis; Atmadeep Banerjee; Cho-Jui Hsieh; Rohit Babbar (KDD, 2024)

Navigating Extremes: Dynamic Sparsity in Large Output Spaces. - Nasibullah Nasibullah; Erik Schultheis; Mike Lasby; Yani Ioannou; Rohit Babbar (NeurIPS, 2024)

UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification. - Siddhant Kharbanda; Devaansh Gupta; Gururaj K; Pankaj Malhotra; Cho-Jui Hsieh; Rohit Babbar (CoRR, 2024)

Learning label-label correlations in Extreme Multi-label Classification via Label Features. - Siddhant Kharbanda; Devaansh Gupta; Erik Schultheis; Atmadeep Banerjee; Cho-Jui Hsieh; Rohit Babbar (CoRR, 2024)

Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models. - Jinbin Zhang; Nasib Ullah; Rohit Babbar (CoRR, 2024)

Navigating Extremes: Dynamic Sparsity in Large Output Space. - Nasib Ullah; Erik Schultheis; Mike Lasby; Yani Ioannou; Rohit Babbar (CoRR, 2024)

Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers? - Nasib Ullah; Erik Schultheis; Jinbin Zhang; Rohit Babbar (CoRR, 2024)

Meta-classifier free negative sampling for extreme multilabel classification. - Mohammadreza Qaraei; Rohit Babbar (Mach. Learn., 2024)

Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales. - Jinbin Zhang; Nasib Ullah; Rohit Babbar (COLING, 2025)

How Well Calibrated are Extreme Multi-label Classifiers? An Empirical Analysis. - Nasib Ullah; Erik Schultheis; Jinbin Zhang; Rohit Babbar (KDD (1), 2025)

UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification. - Siddhant Kharbanda; Devaansh Gupta; Gururaj K; Pankaj Malhotra; Amit Singh 003; Cho-Jui Hsieh; Rohit Babbar (WWW, 2025)

Unbiased Loss Functions for Multilabel Classification with Missing Labels. - Erik Schultheis; Rohit Babbar (Trans. Mach. Learn. Res., 2025)