| Deep batch active learning by diverse, uncertain gradient lower bounds JT Ash, C Zhang, A Krishnamurthy, J Langford, A Agarwal arXiv preprint arXiv:1906.03671, 2019 | 725 | 2019 |
| Active learning from weak and strong labelers C Zhang, K Chaudhuri Advances in Neural Information Processing Systems 28, 2015 | 106 | 2015 |
| Beyond disagreement-based agnostic active learning C Zhang, K Chaudhuri Advances in Neural Information Processing Systems 27, 2014 | 95 | 2014 |
| Contextual bandits with continuous actions: Smoothing, zooming, and adapting A Krishnamurthy, J Langford, A Slivkins, C Zhang Journal of Machine Learning Research 21 (137), 1-45, 2020 | 75 | 2020 |
| Revisiting perceptron: Efficient and label-optimal learning of halfspaces S Yan, C Zhang Advances in Neural Information Processing Systems 30, 2017 | 55 | 2017 |
| Efficient active learning of sparse halfspaces C Zhang Conference on Learning Theory, 1856-1880, 2018 | 38 | 2018 |
| Efficient active learning of sparse halfspaces with arbitrary bounded noise C Zhang, J Shen, P Awasthi Advances in Neural Information Processing Systems 33, 7184-7197, 2020 | 34 | 2020 |
| Efficient contextual bandits with continuous actions M Majzoubi, C Zhang, R Chari, A Krishnamurthy, J Langford, A Slivkins Advances in Neural Information Processing Systems 33, 349-360, 2020 | 33 | 2020 |
| Search improves label for active learning A Beygelzimer, DJ Hsu, J Langford, C Zhang Advances in neural information processing systems 29, 2016 | 32 | 2016 |
| Warm-starting contextual bandits: Robustly combining supervised and bandit feedback C Zhang, A Agarwal, H Daumé III, J Langford, SN Negahban arXiv preprint arXiv:1901.00301, 2019 | 30 | 2019 |
| Multitask bandit learning through heterogeneous feedback aggregation Z Wang, C Zhang, MK Singh, L Riek, K Chaudhuri International Conference on Artificial Intelligence and Statistics, 1531-1539, 2021 | 26* | 2021 |
| The extended littlestone’s dimension for learning with mistakes and abstentions C Zhang, K Chaudhuri Conference on learning theory, 1584-1616, 2016 | 26 | 2016 |
| Improved algorithms for efficient active learning halfspaces with massart and tsybakov noise C Zhang, Y Li Conference on Learning Theory, 4526-4527, 2021 | 23 | 2021 |
| Efficient Online Bandit Multiclass Learning with Regret A Beygelzimer, F Orabona, C Zhang International Conference on Machine Learning, 488-497, 2017 | 22 | 2017 |
| Active fairness auditing T Yan, C Zhang International Conference on Machine Learning, 24929-24962, 2022 | 21 | 2022 |
| Active online learning with hidden shifting domains Y Chen, H Luo, T Ma, C Zhang International Conference on Artificial Intelligence and Statistics, 2053-2061, 2021 | 18* | 2021 |
| Provably efficient multi-task reinforcement learning with model transfer C Zhang, Z Wang Advances in Neural Information Processing Systems 34, 19771-19783, 2021 | 17 | 2021 |
| Crush optimism with pessimism: Structured bandits beyond asymptotic optimality KS Jun, C Zhang Advances in Neural Information Processing Systems 33, 6366-6376, 2020 | 17 | 2020 |
| Bandit multiclass linear classification: Efficient algorithms for the separable case A Beygelzimer, D Pal, B Szorenyi, D Thiruvenkatachari, CY Wei, C Zhang International Conference on Machine Learning, 624-633, 2019 | 14 | 2019 |
| Popart: Efficient sparse regression and experimental design for optimal sparse linear bandits K Jang, C Zhang, KS Jun Advances in Neural Information Processing Systems 35, 2102-2114, 2022 | 11 | 2022 |
John LangfordMicrosoft Research New York在 hunch.net 的电子邮件经过验证
