| Maximum mean discrepancy test is aware of adversarial attacks R Gao, F Liu, J Zhang, B Han, T Liu, G Niu, M Sugiyama International Conference on Machine Learning, 3564-3575, 2021 | 102* | 2021 |
| Fast and reliable evaluation of adversarial robustness with minimum-margin attack R Gao, J Wang, K Zhou, F Liu, B Xie, G Niu, B Han, J Cheng International Conference on Machine Learning, 7144-7163, 2022 | 24 | 2022 |
| Scalable continuous-time diffusion framework for network inference and influence estimation K Huang, R Gao, B Cautis, X Xiao Proceedings of the ACM Web Conference 2024, 2660-2671, 2024 | 9 | 2024 |
| An improved method for model-based training, detection and pose estimation of texture-less 3D objects in occlusion scenes D Zou, Q Cao, Z Zhuang, H Huang, R Gao, W Qin Procedia CIRP 83, 541-546, 2019 | 9 | 2019 |
| Local reweighting for adversarial training R Gao, F Liu, K Zhou, G Niu, B Han, J Cheng arXiv preprint arXiv:2106.15776, 2021 | 6 | 2021 |
| Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem J Li, Y Ma, R Gao, Z Cao, A Lim, W Song, J Zhang IEEE Transactions on Cybernetics, 2021 | 4 | 2021 |
| Topic-aware influence maximization with deep reinforcement learning and graph attention networks T Halal, B Cautis, B Groz, R Gao Data Mining and Knowledge Discovery 39 (6), 71, 2025 | 1 | 2025 |
| Privacy Breach Detection by Non-Parametric Two-Sample Tests R Gao, K Huang, F Liu, B Cautis, X Xiao | | |
Gang NiuSenior Research Scientist (tenured), RIKEN Center for Advanced Intelligence ProjectVerified email at riken.jp
