Published in preprint, 2025
Recommended citation: Li, M., Zhang, R., Wen, Z., Pan, S., da Silva, B. C., Zhai, J., & Ma, S. (2025). PROMPTMINER: Black-Box Prompt Stealing against Text-to-Image Generative Models via Reinforcement Learning and Fuzz Optimization. arXiv preprint arXiv:2511.22119. https://arxiv.org/abs/2511.22119
Published in preprint, 2025
Recommended citation: Chaudhari*, S. , Zhang*, R., Thomas, P. S., & da Silva, B. C. (2025). Which Rewards Matter? Reward Selection for Reinforcement Learning under Limited Feedback. arXiv preprint arXiv:2510.00144. https://arxiv.org/abs/2510.00144
Published in Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), 2024
Recommended citation: Jones, R. K., Zhang, R., Ganeshan, A., & Ritchie, D. (2024). Learning to Edit Visual Programs with Self-Supervision. Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://arxiv.org/abs/2406.02383
Published in Forty-first International Conference on Machine Learning (ICML), 2024
Recommended citation: Zhang, R., Fu, H., Miao, Y., & Konidaris, G. (2024). Model-based Reinforcement Learning for Parameterized Action Spaces. arXiv preprint arXiv:2404.03037. https://arxiv.org/abs/2404.03037
Published in IEEE Transactions on Vehicular Technology (IEEE TVT), 2022
Recommended citation: R. Zhang, X. Li and N. Zhao, "When DSA Meets SWIPT: A Joint Power Allocation and Time Splitting Scheme Based on Multi-Agent Deep Reinforcement Learning," in IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 2740-2744, Feb. 2023, doi: 10.1109/TVT.2022.3213243. https://ieeexplore.ieee.org/document/9915473
Published in Association for the Advancement of Artificial Intelligence (AAAI2021), 2021
Recommended citation: Liu, S., Zhang, A., Li, Y., Zhou, J., Xu, L., Dong, Z., & Zhang, R. (2021, May). Temporal segmentation of fine-gained semantic action: A motion-centered figure skating dataset. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 3, pp. 2163-2171). https://doi.org/10.1609/aaai.v35i3.16314
Published in National Intellectual Property Administration, PRC (China Patent), 2021
Recommended citation: Li, X., Dong, Y., Zhang, Y., Sun, H., Zhang, R., Ding, H., An Intelligent Cost-Efficient Skipping Based Sensing Scheme for Cognitive Radio Transmissions, CN112367131A,2021-02-12 https://patents.google.com/patent/CN112367131A