| 2026 | ACL | Synthetic Data Generation for Training Diversified Commonsense Reasoning Models. | Tianhui Zhang, Bei Peng, Danushka Bollegala |
| 2025 | ACL | Evaluating the Evaluation of Diversity in Commonsense Generation. | Tianhui Zhang, Bei Peng, Danushka Bollegala |
| 2025 | CVPR | SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model. | Zhenglin Huang, Jinwei Hu, Xiangtai Li, Yiwei He, Xingyu Zhao, Bei Peng, Baoyuan Wu, Xiaowei Huang, Guangliang Cheng |
| 2025 | IROS | LSTM-MHSA-Enhanced Deep Reinforcement Learning for Accurate Gait Control in Human Musculoskeletal Model. | Shiyu Mao, Zihao Tang, Fanny Ficuciello, Bei Peng, Dunwen Wei |
| 2024 | EMNLP | Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning. | Tianhui Zhang, Bei Peng, Danushka Bollegala |
| 2024 | SGAI | Contextual Transformers for Goal-Oriented Reinforcement Learning. | Oliver Dippel, Alexei Lisitsa, Bei Peng |
| 2023 | IJCNLP | Learning to Predict Concept Ordering for Common Sense Generation. | Tianhui Zhang, Danushka Bollegala, Bei Peng |
| 2023 | SGAI | Deep Reinforcement Learning for Continuous Control of Material Thickness. | Oliver Dippel, Alexei Lisitsa, Bei Peng |
| 2021 | ICLR | RODE: Learning Roles to Decompose Multi-Agent Tasks. | Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, Chongjie Zhang |
| 2021 | ICML | UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning. | Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson |
| 2021 | ICML | Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. | Shariq Iqbal, Christian A. Schrder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha |
| 2020 | ICLR | Optimistic Exploration even with a Pessimistic Initialisation. | Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson |
| 2017 | ICML | Interactive Learning from Policy-Dependent Human Feedback. | James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman |
| 2015 | AAAI | Generating Real-Time Crowd Advice to Improve Reinforcement Learning Agents. | Gabriel Victor de la Cruz, Bei Peng, Walter Stephen Lasecki, Matthew Edmund Taylor |
| 2015 | IUI | Towards Integrating Real-Time Crowd Advice with Reinforcement Learning. | Gabriel Victor de la Cruz, Bei Peng, Walter S. Lasecki, Matthew E. Taylor |
| 2014 | AAAI | A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. | Robert Tyler Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, Jeff Huang, David L. Roberts |
| 2014 | RO-MAN | Learning something from nothing: Leveraging implicit human feedback strategies. | Robert Tyler Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, David L. Roberts |
| 2012 | APWEB | A GPU-Based Accelerator for Chinese Word Segmentation. | Xiwu Gu, Ruixuan Li, Kunmei Wen, Bei Peng, Weijun Xiao |