| Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions S Atakishiyev, M Salameh, H Yao, R Goebel arXiv preprint arXiv:2112.11561, 2021 | 119 | 2021 |
| Distributional Reinforcement Learning for Efficient Exploration B Mavrin, S Zhang, H Yao, K Kong, Linglong, Wu, Y Yu https://arxiv.org/abs/1905.06125, 2019 | 93 | 2019 |
| Negative log likelihood ratio loss for deep neural network classification H Yao, D Zhu, B Jiang, P Yu Proceedings of the Future Technologies Conference (FTC) 2019: Volume 1, 276-282, 2020 | 91 | 2020 |
| Discounted reinforcement learning is not an optimization problem A Naik, R Shariff, N Yasui, H Yao, RS Sutton arXiv preprint arXiv:1910.02140, 2019 | 65 | 2019 |
| Mapless navigation among dynamics with social-safety-awareness: a reinforcement learning approach from 2d laser scans J Jin, NM Nguyen, N Sakib, D Graves, H Yao, M Jagersand 2020 IEEE international conference on robotics and automation (ICRA), 6979-6985, 2020 | 60 | 2020 |
| Provably convergent two-timescale off-policy actor-critic with function approximation S Zhang, B Liu, H Yao, S Whiteson International Conference on Machine Learning, 11204-11213, 2020 | 54 | 2020 |
| Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings M Siam, N Doraiswamy, BN Oreshkin, H Yao, M Jagersand arXiv preprint arXiv:2001.09540, 2020 | 49 | 2020 |
| Universal Option Models H Yao, C Szepesvari, R Sutton, S Bhatnagar, J Modayil | 45* | 2014 |
| Breaking the deadly triad with a target network S Zhang, H Yao, S Whiteson International Conference on Machine Learning, 12621-12631, 2021 | 42 | 2021 |
| A multi-component framework for the analysis and design of explainable artificial intelligence MY Kim, S Atakishiyev, HKB Babiker, N Farruque, R Goebel, OR Zaïane, ... Machine Learning and Knowledge Extraction 3 (4), 900-921, 2021 | 41 | 2021 |
| Method of prediction of a state of an object in the environment using an action model of a neural network H Yao, SM Nosrati, H Chen, P Yadmellat, Y Zhang US Patent 10,997,491, 2021 | 41 | 2021 |
| Multi-step dyna planning for policy evaluation and control H Yao, S Bhatnagar, D Diao Advances in neural information processing systems 22, 2009 | 35* | 2009 |
| Quota: The quantile option architecture for reinforcement learning S Zhang, H Yao Proceedings of the AAAI conference on artificial intelligence 33 (01), 5797-5804, 2019 | 32 | 2019 |
| Ace: An actor ensemble algorithm for continuous control with tree search S Zhang, H Yao Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5789-5796, 2019 | 31 | 2019 |
| Pseudo-MDPs and Factored Linear Action Models H Yao, C Szepesvari, BA Pires, X Zhang IEEE ADPRL, 2014 | 27 | 2014 |
| Method of selection of an action for an object using a neural network H Yao, H Chen, SM Nosrati, P Yadmellat, Y Zhang US Patent 10,935,982, 2021 | 24 | 2021 |
| Approximate policy iteration with linear action models H Yao, C Szepesvári Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1212-1218, 2012 | 18 | 2012 |
| Preconditioned temporal difference learning H Yao, ZQ Liu Proceedings of the 25th international conference on Machine learning, 1208-1215, 2008 | 18 | 2008 |
| Hill climbing on value estimates for search-control in Dyna Y Pan, H Yao, A Farahmand, M White arXiv preprint arXiv:1906.07791, 2019 | 17 | 2019 |
| Towards practical hierarchical reinforcement learning for multi-lane autonomous driving MS Nosrati, EA Abolfathi, M Elmahgiubi, P Yadmellat, J Luo, Y Zhang, ... | 16 | 2018 |
Linglong KongProfessor, Canada Research Chair in Statistical Learning, UAlberta, and Canada CIFAR AI Chair, Amii在 ualberta.ca 的电子邮件经过验证
