| Hyperband: A novel bandit-based approach to hyperparameter optimization L Li, K Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar The Journal of Machine Learning Research 18 (1), 6765-6816, 2017 | 2543* | 2017 |
| Hyperband: Bandit-based Configuration Evaluation for Hyperparameter Optimization AT Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh ICLR, 2017 | 2086* | 2017 |
| Learning with rejection C Cortes, G DeSalvo, M Mohri Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari …, 2016 | 208 | 2016 |
| Efficient hyperparameter optimization and infinitely many armed bandits L Li, KG Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar CoRR, abs/1603.06560 16, 2016 | 135 | 2016 |
| Boosting with abstention C Cortes, G DeSalvo, M Mohri Advances in Neural Information Processing Systems 29, 2016 | 84 | 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 | 54 | 2021 |
| Online learning with abstention C Cortes, G DeSalvo, C Gentile, M Mohri, S Yang International conference on machine learning, 1059-1067, 2018 | 35 | 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 | 29 | 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 | 26 | 2014 |
| Active learning with disagreement graphs C Cortes, G DeSalvo, M Mohri, N Zhang, C Gentile International Conference on Machine Learning, 1379-1387, 2019 | 23 | 2019 |
| Discrepancy-based algorithms for non-stationary rested bandits C Cortes, G DeSalvo, V Kuznetsov, M Mohri, S Yang arXiv preprint arXiv:1710.10657, 2017 | 18 | 2017 |
| 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 |
| Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. arXiv 2018 L Li, K Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar arXiv preprint arXiv:1603.06560, 2016 | 16 | 2016 |
| Learning with deep cascades G DeSalvo, M Mohri, U Syed Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff …, 2015 | 16 | 2015 |
| Adaptive region-based active learning C Cortes, G DeSalvo, C Gentile, M Mohri, N Zhang International Conference on Machine Learning, 2144-2153, 2020 | 10 | 2020 |
| Random composite forests G DeSalvo, M Mohri Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 8 | 2016 |
| 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 | 7 | 2020 |
| Efficient hyperparameter optimization and infinitely many armed bandits A Rostamizadeh, A Talwalkar, G DeSalvo, K Jamieson, L Li | 7 | 2017 |
| 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 | 6 | 2020 |
| Multi-armed bandits with non-stationary rewards C Cortes, G DeSalvo, V Kuznetsov, M Mohri, S Yand CoRR, abs/1710.10657 1, 2017 | 5 | 2017 |
