| 2025 | COLING | Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales. | Jinbin Zhang, Nasib Ullah, Rohit Babbar |
| 2025 | ICML | ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces. | Jinbin Zhang, Nasib Ullah, Erik Schultheis, Rohit Babbar |
| 2025 | KDD | How Well Calibrated are Extreme Multi-label Classifiers? An Empirical Analysis. | Nasib Ullah, Erik Schultheis, Jinbin Zhang, Rohit Babbar |
| 2025 | WWW | UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification. | Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Amit Singh, Cho-Jui Hsieh, Rohit Babbar |
| 2024 | ICLR | Consistent algorithms for multi-label classification with macro-at-k metrics. | Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski |
| 2024 | ICML | A General Online Algorithm for Optimizing Complex Performance Metrics. | Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski |
| 2024 | KDD | 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 |
| 2023 | SIGIR | InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification. | Siddhant Kharbanda, Atmadeep Banerjee, Devaansh Gupta, Akash Palrecha, Rohit Babbar |
| 2022 | KDD | On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification. | Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczynski |
| 2021 | WWW | Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels. | Mohammadreza Qaraei, Erik Schultheis, Priyanshu Gupta, Rohit Babbar |
| 2021 | SIGIR | Propensity-scored Probabilistic Label Trees. | Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar, Krzysztof Dembczynski |
| 2020 | ESANN | 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 |
| 2020 | ICONIP | Neural Architecture Search for Extreme Multi-label Text Classification. | Loc Pauletto, Massih-Reza Amini, Rohit Babbar, Nicolas Winckler |
| 2020 | INFOCOM | Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading. | Thaha Mohammed, Carlee Joe-Wong, Rohit Babbar, Mario Di Francesco |
| 2019 | ESANN | A Simple and Effective Scheme for Data Pre-processing in Extreme Classification. | Sujay Khandagale, Rohit Babbar |
| 2017 | WSDM | DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. | Rohit Babbar, Bernhard Schlkopf |
| 2016 | SDM | TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. | Rohit Babbar, Krikamol Muandet, Bernhard Schlkopf |
| 2015 | IDA | Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data. | Georgios Balikas, Ioannis Partalas, ric Gaussier, Rohit Babbar, Massih-Reza Amini |
| 2014 | SIGIR | Re-ranking approach to classification in large-scale power-law distributed category systems. | Rohit Babbar, Ioannis Partalas, ric Gaussier, Massih-Reza Amini |
| 2013 | ICONIP | Maximum-Margin Framework for Training Data Synchronization in Large-Scale Hierarchical Classification. | Rohit Babbar, Ioannis Partalas, ric Gaussier, Massih-Reza Amini |
| 2012 | CIKM | On empirical tradeoffs in large scale hierarchical classification. | Rohit Babbar, Ioannis Partalas, ric Gaussier, Ccile Amblard |
| 2012 | ICONIP | Adaptive Classifier Selection in Large-Scale Hierarchical Classification. | Ioannis Partalas, Rohit Babbar, ric Gaussier, Ccile Amblard |