| Batch active learning at scale G Citovsky, G DeSalvo, C Gentile, L Karydas, A Rajagopalan, ... Advances in Neural Information Processing Systems 34, 11933-11944, 2021 | 233 | 2021 |
| Scaling hierarchical agglomerative clustering to billion-sized datasets B Sumengen, A Rajagopalan, G Citovsky, D Simcha, O Bachem, P Mitra, ... arXiv preprint arXiv:2105.11653, 2021 | 30 | 2021 |
| Online hierarchical clustering approximations AK Menon, A Rajagopalan, B Sumengen, G Citovsky, Q Cao, S Kumar arXiv preprint arXiv:1909.09667, 2019 | 22 | 2019 |
| Hierarchical clustering of data streams: Scalable algorithms and approximation guarantees A Rajagopalan, F Vitale, D Vainstein, G Citovsky, CM Procopiuc, ... International conference on machine learning, 8799-8809, 2021 | 16 | 2021 |
| Hierarchical clustering via sketches and hierarchical correlation clustering D Vainstein, V Chatziafratis, G Citovsky, A Rajagopalan, M Mahdian, ... International Conference on Artificial Intelligence and Statistics, 559-567, 2021 | 12 | 2021 |
| Flattening a hierarchical clustering through active learning F Vitale, A Rajagopalan, C Gentile Advances in Neural Information Processing Systems 32, 2019 | 10 | 2019 |
| Outlier eigenvalue fluctuations of perturbed iid matrices AB Rajagopalan University of California, Los Angeles, 2015 | 7 | 2015 |
| Schauder Bases for Using ReLU, Softplus and Two Sigmoidal Functions A Ganesh, B Bose, A Rajagopalan arXiv preprint arXiv:2506.07884, 2025 | | 2025 |
| A Singular Integral for a Simplified Clairaut Equation A Ganesh, A Rajagopalan arXiv preprint arXiv:2409.16229, 2024 | | 2024 |
| On the Approximability of Stationary Processes using the ARMA Model A Ganesh, B Bose, A Rajagopalan arXiv preprint arXiv:2408.10610, 2024 | | 2024 |
| Batch Active Learning at Scale A Rostamizadeh, A Rajagopalan, C Gentile, G DeSalvo, G Citovsky, ... | | 2021 |
