| 2025 | FOCS | Theoretical limitations of multi-layer Transformer. | Lijie Chen, Binghui Peng, Hongxun Wu |
| 2025 | ICML | A Near Linear Query Lower Bound for Submodular Maximization. | Binghui Peng, Aviad Rubinstein |
| 2024 | ALT | The complexity of non-stationary reinforcement learning. | Binghui Peng, Christos H. Papadimitriou |
| 2024 | COLT | The sample complexity of multi-distribution learning. | Binghui Peng |
| 2024 | COLT | The complexity of approximate (coarse) correlated equilibrium for incomplete information games. | Binghui Peng, Aviad Rubinstein |
| 2024 | STOC | Fast Swap Regret Minimization and Applications to Approximate Correlated Equilibria. | Binghui Peng, Aviad Rubinstein |
| 2023 | ESA | Primal-Dual Schemes for Online Matching in Bounded Degree Graphs. | Ilan Reuven Cohen, Binghui Peng |
| 2023 | FOCS | Memory-Query Tradeoffs for Randomized Convex Optimization. | Xi Chen, Binghui Peng |
| 2023 | FOCS | The Complexity of Dynamic Least-Squares Regression. | Shunhua Jiang, Binghui Peng, Omri Weinstein |
| 2023 | FOCS | Near Optimal Memory-Regret Tradeoff for Online Learning. | Binghui Peng, Aviad Rubinstein |
| 2023 | SODA | Online Prediction in Sub-linear Space. | Binghui Peng, Fred Zhang |
| 2023 | STOC | Complexity of Equilibria in First-Price Auctions under General Tie-Breaking Rules. | Xi Chen, Binghui Peng |
| 2022 | FOCS | Memory Bounds for Continual Learning. | Xi Chen, Christos H. Papadimitriou, Binghui Peng |
| 2022 | ICLR | Shuffle Private Stochastic Convex Optimization. | Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng |
| 2022 | SODA | Robust Load Balancing with Machine Learned Advice. | Sara Ahmadian, Hossein Esfandiari, Vahab S. Mirrokni, Binghui Peng |
| 2022 | SODA | Computational Hardness of the Hylland-Zeckhauser Scheme. | Thomas Chen, Xi Chen, Binghui Peng, Mihalis Yannakakis |
| 2022 | STOC | On the complexity of dynamic submodular maximization. | Xi Chen, Binghui Peng |
| 2021 | ACL | Self-Attention Networks Can Process Bounded Hierarchical Languages. | Shunyu Yao, Binghui Peng, Christos H. Papadimitriou, Karthik Narasimhan |
| 2021 | ICLR | MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. | Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, Christopher R |
| 2020 | AAAI | Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization. | Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao |
| 2020 | AAAI | Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions. | Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, Pingzhong Tang |
| 2019 | AAAI | Learning Optimal Strategies to Commit To. | Binghui Peng, Weiran Shen, Pingzhong Tang, Song Zuo |
| 2019 | FOCS | Tight Bounds for Online Edge Coloring. | Ilan Reuven Cohen, Binghui Peng, David Wajc |
| 2019 | ICALP | Stochastic Online Metric Matching. | Anupam Gupta, Guru Guruganesh, Binghui Peng, David Wajc |
| 2019 | ISAAC | On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback. | Wei Chen, Binghui Peng |
| 2019 | SODA | Tight Competitive Ratios of Classic Matching Algorithms in the Fully Online Model. | Zhiyi Huang, Binghui Peng, Zhihao Gavin Tang, Runzhou Tao, Xiaowei Wu, Yuhao Zhang |