| Hyperband: A novel bandit-based approach to hyperparameter optimization L Li, K Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar Journal of Machine Learning Research 18 (185), 1-52, 2018 | 3415* | 2018 |
| Learning with rejection C Cortes, G DeSalvo, M Mohri Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari …, 2016 | 300 | 2016 |
| Hyperband: Bandit-based Configuration Evaluation for Hyperparameter Optimization AT Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh ICLR, 2017 | 172* | 2017 |
| Efficient hyperparameter optimization and infinitely many armed bandits L Li, KG Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar CoRR, abs/1603.06560 16, 2016 | 153 | 2016 |
| 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 | 135 | 2021 |
| Boosting with abstention C Cortes, G DeSalvo, M Mohri Advances in Neural Information Processing Systems 29, 2016 | 130 | 2016 |
| Online learning with abstention C Cortes, G DeSalvo, C Gentile, M Mohri, S Yang international conference on machine learning, 1059-1067, 2018 | 49 | 2018 |
| Region-based active learning C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 35 | 2019 |
| Precise measurement of laser power using an optomechanical system K Agatsuma, D Friedrich, S Ballmer, G DeSalvo, S Sakata, E Nishida, ... Optics express 22 (2), 2013-2030, 2014 | 33 | 2014 |
| Active learning with disagreement graphs C Cortes, G DeSalvo, M Mohri, N Zhang, C Gentile International Conference on Machine Learning, 1379-1387, 2019 | 26 | 2019 |
| Learning with deep cascades G DeSalvo, M Mohri, U Syed Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff …, 2015 | 22 | 2015 |
| Discrepancy-based algorithms for non-stationary rested bandits C Cortes, G DeSalvo, V Kuznetsov, M Mohri, S Yang arXiv preprint arXiv:1710.10657, 2017 | 20 | 2017 |
| Theory and algorithms for learning with rejection in binary classification C Cortes, G DeSalvo, M Mohri Annals of Mathematics and Artificial Intelligence 92 (2), 277-315, 2024 | 16 | 2024 |
| Online learning with sleeping experts and feedback graphs C Cortes, G DeSalvo, C Gentile, M Mohri, S Yang International Conference on Machine Learning, 1370-1378, 2019 | 16 | 2019 |
| Adaptive region-based active learning C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang International Conference on Machine Learning, 2144-2153, 2020 | 15 | 2020 |
| Online learning with dependent stochastic feedback graphs C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang International Conference on Machine Learning, 2154-2163, 2020 | 13 | 2020 |
| Random composite forests G DeSalvo, M Mohri Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 11 | 2016 |
| Understanding the effects of batching in online active learning K Amin, C Cortes, G DeSalvo, A Rostamizadeh International Conference on Artificial Intelligence and Statistics, 3482-3492, 2020 | 10 | 2020 |
| Firebolt: Weak supervision under weaker assumptions Z Kuang, CG Arachie, B Liang, P Narayana, G DeSalvo, MS Quinn, ... International Conference on Artificial Intelligence and Statistics, 8214-8259, 2022 | 9 | 2022 |
| Agile modeling: From concept to classifier in minutes O Stretcu, E Vendrow, K Hata, K Viswanathan, V Ferrari, S Tavakkol, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 8 | 2023 |
