| Off-Policy Actor-Critic T Degris, M White, RS Sutton Twenty-Ninth International Conference on Machine Learning, 2012 | 432 | 2012 |
| An emphatic approach to the problem of off-policy temporal-difference learning RS Sutton, AR Mahmood, M White The Journal of Machine Learning Research 17 (1), 2603-2631, 2016 | 202 | 2016 |
| Meta-learning representations for continual learning K Javed, M White Advances in Neural Information Processing Systems 32, 2019 | 197 | 2019 |
| Convex multi-view subspace learning M White, X Zhang, D Schuurmans, Y Yu Advances in neural information processing systems 25, 2012 | 162 | 2012 |
| Supervised autoencoders: Improving generalization performance with unsupervised regularizers L Le, A Patterson, M White Advances in neural information processing systems 31, 2018 | 161 | 2018 |
| Estimating the class prior and posterior from noisy positives and unlabeled data S Jain, M White, P Radivojac Advances in neural information processing systems 29, 2016 | 95 | 2016 |
| Unifying task specification in reinforcement learning M White International Conference on Machine Learning, 2016 | 77 | 2016 |
| Maxmin Q-learning: Controlling the Estimation Bias of Q-learning Q Lan, Y Pan, A Fyshe, M White International Conference on Learning Representations, 2020 | 67 | 2020 |
| An off-policy policy gradient theorem using emphatic weightings E Imani, E Graves, M White Advances in Neural Information Processing Systems 31, 2018 | 55 | 2018 |
| Recovering true classifier performance in positive-unlabeled learning S Jain, M White, P Radivojac Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 53 | 2017 |
| Nonparametric semi-supervised learning of class proportions S Jain, M White, MW Trosset, P Radivojac arXiv preprint arXiv:1601.01944, 2016 | 52 | 2016 |
| Relaxed clipping: A global training method for robust regression and classification Y Yu, M Yang, L Xu, M White, D Schuurmans Advances in Neural Information Processing Systems 23, 2011 | 47 | 2011 |
| Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains Y Pan, M Zaheer, A White, A Patterson, M White International Joint Conference on Artificial Intelligence, 2018 | 43 | 2018 |
| A greedy approach to adapting the trace parameter for temporal difference learning M White, A White International Conference on Autonomous Agents & Multiagent Systems, 557-565, 2016 | 38 | 2016 |
| The utility of sparse representations for control in reinforcement learning V Liu, R Kumaraswamy, L Le, M White AAAI Conference on Artificial Intelligence, 2019 | 37 | 2019 |
| Investigating practical, linear temporal difference learning A White, M White Autonomous Agents and Multiagent Sytems, 2016 | 37 | 2016 |
| Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. X Zhang, Y Yu, M White, R Huang, D Schuurmans Proceedings of the AAAI Conference on Artificial Intelligence, 2011 | 37 | 2011 |
| Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online Y Pan, K Banman, M White International Conference on Learning Representations, 2021 | 31* | 2021 |
| Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains M White, A White Advances in Neural Information Processing Systems, 2433–2441, 2010 | 31 | 2010 |
| Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning L Xu, M White, D Schuurmans Proceedings of the 26th International Conference on Machine Learning, 1137-1144, 2009 | 31 | 2009 |
Adam WhiteUniversity of Alberta, Deepmindualberta.ca의 이메일 확인됨
