| 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 |
| 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 |
| 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 |