| 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 | 36* | 2021 |
| 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 | 35 | 2022 |
| 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, 2074-2087, 2022 | 31 | 2022 |
| Speeding-up one-versus-all training for extreme classification via mean-separating initialization E Schultheis, R Babbar Machine Learning, 1-24, 2022 | 20* | 2022 |
| Channeling of branched flow in weakly scattering anisotropic media H Degueldre, JJ Metzger, E Schultheis, R Fleischmann Physical Review Letters 118 (2), 024301, 2017 | 20 | 2017 |
| Unbiased Loss Functions for Multilabel Classification with Missing Labels E Schultheis, R Babbar arXiv preprint arXiv:2109.11282, 2021 | 7 | 2021 |
| Generalized test utilities for long-tail performance in extreme multi-label classification E Schultheis, M Wydmuch, W Kotlowski, R Babbar, K Dembczynski Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Learning label-label correlations in Extreme Multi-label Classification via Label Features S Kharbanda, D Gupta, E Schultheis, A Banerjee, CJ Hsieh, R Babbar arXiv preprint arXiv:2405.04545, 2024 | 3* | 2024 |
| Consistent algorithms for multi-label classification with macro-at- metrics E Schultheis, W Kotłowski, M Wydmuch, R Babbar, S Borman, ... arXiv preprint arXiv:2401.16594, 2024 | 3 | 2024 |
| Towards Memory-Efficient Training for Extremely Large Output Spaces–Learning with 670k Labels on a Single Commodity GPU E Schultheis, R Babbar Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 3 | 2023 |
| Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers? N Ullah, E Schultheis, J Zhang, R Babbar arXiv preprint arXiv:2411.04276, 2024 | | 2024 |
| LLaMA-Annotate—Visualizing Token-Level Confidences for LLMs E Schultheis, ST John Joint European Conference on Machine Learning and Knowledge Discovery in …, 2024 | | 2024 |
| A General Online Algorithm for Optimizing Complex Performance Metrics W Kotłowski, M Wydmuch, E Schultheis, R Babbar, K Dembczyński arXiv preprint arXiv:2406.14743, 2024 | | 2024 |
| 6.3 Extreme Multicore Classification E Schultheis, R Babbar Also of interest, 272, 2022 | | 2022 |
| Beyond Standard Performance Measures in Extreme Multi-label Classification E Schultheis, M Wydmuch, R Babbar, K Dembczyński Workshop on Online and Adaptive Recommender Systems, 2022 | | 2022 |
| Unbiased Estimates for Multilabel Reductions of Extreme Classification with Missing Labels E Schultheis, R Babbar | | |
Rohit BabbarUniversity of Bath, UK and Aalto University, Helsinki, Finland確認したメール アドレス: aalto.fi
