| 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 | 3040 | 2017 |
| Non-stochastic best arm identification and hyperparameter optimization K Jamieson, A Talwalkar Artificial intelligence and statistics, 240-248, 2016 | 724 | 2016 |
| A system for massively parallel hyperparameter tuning L Li, K Jamieson, A Rostamizadeh, E Gonina, J Ben-Tzur, M Hardt, ... Proceedings of Machine Learning and Systems 2, 230-246, 2020 | 608* | 2020 |
| lil’ucb: An optimal exploration algorithm for multi-armed bandits K Jamieson, M Malloy, R Nowak, S Bubeck Conference on Learning Theory, 423-439, 2014 | 484 | 2014 |
| Active ranking using pairwise comparisons KG Jamieson, R Nowak Advances in neural information processing systems 24, 2011 | 283 | 2011 |
| Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting K Jamieson, R Nowak 2014 48th annual conference on information sciences and systems (CISS), 1-6, 2014 | 227 | 2014 |
| Query complexity of derivative-free optimization KG Jamieson, B Recht, R Nowak Advances in Neural Information Processing Systems, 2672-2680, 2012 | 191 | 2012 |
| Hyperband: Bandit-based configuration evaluation for hyperparameter optimization. L Li, KG Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar ICLR (Poster), 53, 2017 | 182 | 2017 |
| Non-asymptotic gap-dependent regret bounds for tabular mdps M Simchowitz, KG Jamieson Advances in Neural Information Processing Systems 32, 2019 | 175 | 2019 |
| Sequential experimental design for transductive linear bandits T Fiez, L Jain, KG Jamieson, L Ratliff Advances in neural information processing systems 32, 2019 | 126 | 2019 |
| Low-dimensional embedding using adaptively selected ordinal data KG Jamieson, RD Nowak 2011 49th annual allerton conference on communication, control, and …, 2011 | 92 | 2011 |
| Top arm identification in multi-armed bandits with batch arm pulls KS Jun, K Jamieson, R Nowak, X Zhu Artificial Intelligence and Statistics, 139-148, 2016 | 90 | 2016 |
| Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations M Laskey, C Chuck, J Lee, J Mahler, S Krishnan, K Jamieson, A Dragan, ... 2017 IEEE International Conference on Robotics and Automation (ICRA), 358-365, 2017 | 84 | 2017 |
| Next: A system for real-world development, evaluation, and application of active learning KG Jamieson, L Jain, C Fernandez, NJ Glattard, R Nowak Advances in neural information processing systems 28, 2015 | 81 | 2015 |
| The simulator: Understanding adaptive sampling in the moderate-confidence regime M Simchowitz, K Jamieson, B Recht Conference on Learning Theory, 1794-1834, 2017 | 76 | 2017 |
| Finite sample prediction and recovery bounds for ordinal embedding L Jain, KG Jamieson, R Nowak Advances in neural information processing systems 29, 2016 | 76 | 2016 |
| A framework for multi-a (rmed)/b (andit) testing with online fdr control F Yang, A Ramdas, KG Jamieson, MJ Wainwright Advances in Neural Information Processing Systems 30, 2017 | 75 | 2017 |
| Sparse dueling bandits K Jamieson, S Katariya, A Deshpande, R Nowak Artificial Intelligence and Statistics, 416-424, 2015 | 74 | 2015 |
| Reward-free rl is no harder than reward-aware rl in linear markov decision processes AJ Wagenmaker, Y Chen, M Simchowitz, S Du, K Jamieson International Conference on Machine Learning, 22430-22456, 2022 | 61 | 2022 |
| Active learning for identification of linear dynamical systems A Wagenmaker, K Jamieson Conference on Learning Theory, 3487-3582, 2020 | 59 | 2020 |
Robert D NowakKeith and Jane Morgan Nosbusch Professor in ECE, Grace Wahba Professor of Data ScienceVerified email at wisc.edu
