| 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 | 176 | 2021 |
| Choice is hard EM Arkin, A Banik, P Carmi, G Citovsky, MJ Katz, JSB Mitchell, M Simakov International Symposium on Algorithms and Computation, 318-328, 2015 | 24 | 2015 |
| 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 | 22 | 2021 |
| Conflict-free Covering. EM Arkin, A Banik, P Carmi, G Citovsky, MJ Katz, JSB Mitchell, M Simakov CCCG, 2015 | 21 | 2015 |
| Online hierarchical clustering approximations AK Menon, A Rajagopalan, B Sumengen, G Citovsky, Q Cao, S Kumar arXiv preprint arXiv:1909.09667, 2019 | 18 | 2019 |
| Exact and approximation algorithms for data mule scheduling in a sensor network G Citovsky, J Gao, JSB Mitchell, J Zeng Algorithms for Sensor Systems: 11th International Symposium on Algorithms …, 2015 | 16 | 2015 |
| 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 | 13 | 2021 |
| Selecting and covering colored points EM Arkin, A Banik, P Carmi, G Citovsky, MJ Katz, JSB Mitchell, M Simakov Discrete Applied Mathematics 250, 75-86, 2018 | 13 | 2018 |
| 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 | 11 | 2021 |
| TSP with locational uncertainty: the adversarial model G Citovsky, T Mayer, JSB Mitchell arXiv preprint arXiv:1705.06180, 2017 | 11 | 2017 |
| Network optimization on partitioned pairs of points EM Arkin, A Banik, P Carmi, G Citovsky, S Jia, MJ Katz, T Mayer, ... arXiv preprint arXiv:1710.00876, 2017 | 5 | 2017 |
| Leveraging importance weights in subset selection G Citovsky, G DeSalvo, S Kumar, S Ramalingam, A Rostamizadeh, ... arXiv preprint arXiv:2301.12052, 2023 | 3 | 2023 |
| A novel view of suprathreshold stochastic resonance and its applications to financial markets G Citovsky, S Focardi Frontiers in Applied Mathematics and Statistics 1, 10, 2015 | 3 | 2015 |
| Exploiting Geometry in the SINR Model R Aschner, G Citovsky, MJ Katz International Symposium on Algorithms and Experiments for Sensor Systems …, 2014 | 2 | 2014 |
| Analyzing Similarity Metrics for Data Selection for Language Model Pretraining D Sam, A Chakrabarti, A Rostamizadeh, S Ramalingam, G Citovsky, ... arXiv preprint arXiv:2502.02494, 2025 | | 2025 |
| GIST: Greedy Independent Set Thresholding for Diverse Data Summarization M Fahrbach, S Ramalingam, M Zadimoghaddam, S Ahmadian, ... arXiv preprint arXiv:2405.18754, 2024 | | 2024 |
| SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection K Ye, H Jiang, A Rostamizadeh, A Chakrabarti, G DeSalvo, JF Kagy, ... arXiv preprint arXiv:2401.13160, 2024 | | 2024 |
| Batch Active Learning at Scale A Rostamizadeh, C Gentile, G DeSalvo, G Citovsky, L Karydas, S Kumar | | 2021 |
| Online Hierarchical Clustering Approximations A Krishna Menon, A Rajagopalan, B Sumengen, G Citovsky, Q Cao, ... arXiv e-prints, arXiv: 1909.09667, 2019 | | 2019 |
| 28th International Symposium on Algorithms and Computation (ISAAC 2017) Y Okamoto, T Tokuyama, S Iwata, S Venkatasubramanian, HK Ahn, ... Schloss Dagstuhl-Leibniz-Zentrum für Informatik GmbH, 2017 | | 2017 |
