| Who is the strongest enemy? towards optimal and efficient evasion attacks in deep rl Y Sun, R Zheng, Y Liang, F Huang arXiv preprint arXiv:2106.05087, 2021 | 65 | 2021 |
| Efficient adversarial training without attacking: Worst-case-aware robust reinforcement learning Y Liang, Y Sun, R Zheng, F Huang Advances in Neural Information Processing Systems 35, 22547-22561, 2022 | 44 | 2022 |
| Certifiably robust policy learning against adversarial multi-agent communication Y Sun, R Zheng, P Hassanzadeh, Y Liang, S Feizi, S Ganesh, F Huang The Eleventh International Conference on Learning Representations, 2023 | 13 | 2023 |
| Drm: Mastering visual reinforcement learning through dormant ratio minimization G Xu*, R Zheng*, Y Liang*, X Wang, Z Yuan, T Ji, Y Luo, X Liu, J Yuan, ... arXiv preprint arXiv:2310.19668, 2023 | 12 | 2023 |
| Certifiably robust policy learning against adversarial communication in multi-agent systems Y Sun, R Zheng, P Hassanzadeh, Y Liang, S Feizi, S Ganesh, F Huang arXiv preprint arXiv:2206.10158, 2022 | 11 | 2022 |
| Parallel knowledge transfer in multi-agent reinforcement learning Y Liang, B Li arXiv preprint arXiv:2003.13085, 2020 | 9 | 2020 |
| InstantNet: Automated generation and deployment of instantaneously switchable-precision networks Y Fu, Z Yu, Y Zhang, Y Jiang, C Li, Y Liang, M Jiang, Z Wang, Y Lin 2021 58th ACM/IEEE Design Automation Conference (DAC), 757-762, 2021 | 5 | 2021 |
| Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations Y Liang, Y Sun, R Zheng, X Liu, T Sandholm, F Huang, S McAleer arXiv preprint arXiv:2307.12062, 2023 | 4 | 2023 |
| Fdnas: Improving data privacy and model diversity in automl C Zhang, Y Liang, X Yuan, L Cheng arXiv preprint arXiv:2011.03372, 2020 | 3 | 2020 |
| Premier-taco: Pretraining multitask representation via temporal action-driven contrastive loss R Zheng, Y Liang, X Wang, S Ma, H Daumé III, H Xu, J Langford, ... arXiv preprint arXiv:2402.06187, 2024 | 2 | 2024 |
| Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies X Liu, C Deng, Y Sun, Y Liang, F Huang arXiv preprint arXiv:2402.12673, 2024 | 1 | 2024 |
| Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss R Zheng, Y Liang, X Wang, S Ma, H Daumé III, H Xu, J Langford, ... Forty-first International Conference on Machine Learning, 2024 | 1 | 2024 |
| Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion Y Liang, T Xu, K Hu, G Jiang, F Huang, H Xu arXiv preprint arXiv:2407.10973, 2024 | | 2024 |
| Is poisoning a real threat to LLM alignment? Maybe more so than you think P Pathmanathan, S Chakraborty, X Liu, Y Liang, F Huang arXiv preprint arXiv:2406.12091, 2024 | | 2024 |
| ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization T Ji*, Y Liang*, Y Zeng, Y Luo, G Xu, J Guo, R Zheng, F Huang, F Sun, ... arXiv preprint arXiv:2402.14528, 2024 | | 2024 |
| Certifiably Robust Multi-Agent Reinforcement Learning against Adversarial Communication Y Sun, R Zheng, P Hassanzadeh, Y Liang, S Feizi, S Ganesh, F Huang The Eleventh International Conference on Learning Representations, 2023 | | 2023 |
| Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations Y Liang, Y Sun, R Zheng, X Liu, B Eysenbach, T Sandholm, F Huang, ... arXiv preprint arXiv:2307.12062, 2023 | | 2023 |
| is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss R Zheng, Y Liang, X Wang, S Ma, H Daumé III, H Xu, J Langford, ... | | |
Furong HuangAssociate Professor of Computer Science, University of Maryland在 umd.edu 的电子邮件经过验证
