Learning Optimal Strategies to Commit To. - Binghui Peng; Weiran Shen; Pingzhong Tang; Song Zuo (AAAI, 2019)

Tight Bounds for Online Edge Coloring. - Ilan Reuven Cohen; Binghui Peng; David Wajc (FOCS, 2019)

Stochastic Online Metric Matching. - Anupam Gupta 001; Guru Guruganesh; Binghui Peng; David Wajc (ICALP, 2019)

On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback. - Wei Chen 013; Binghui Peng (ISAAC, 2019)

Adaptive Influence Maximization with Myopic Feedback. - Binghui Peng; Wei Chen 013 (NeurIPS, 2019)

Tight Competitive Ratios of Classic Matching Algorithms in the Fully Online Model. - Zhiyi Huang 002; Binghui Peng; Zhihao Gavin Tang; Runzhou Tao; Xiaowei Wu 001; Yuhao Zhang 001 (SODA, 2019)

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 (AAAI, 2020)

Hedging in games: Faster convergence of external and swap regrets. - Xi Chen 001; Binghui Peng (NeurIPS, 2020)

MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery. - Xiaoxiao Li; Yangsibo Huang; Binghui Peng; Zhao Song 002; Kai Li 001 (CoRR, 2020)

Self-Attention Networks Can Process Bounded Hierarchical Languages. - Shunyu Yao; Binghui Peng; Christos H. Papadimitriou; Karthik Narasimhan (ACL/IJCNLP (1), 2021)

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 002; Anshumali Shrivastava; Christopher Ré (ICLR, 2021)

Distributed Load Balancing: A New Framework and Improved Guarantees. - Sara Ahmadian; Allen Liu; Binghui Peng; Morteza Zadimoghaddam (ITCS, 2021)

Training (Overparametrized) Neural Networks in Near-Linear Time. - Jan van den Brand; Binghui Peng; Zhao Song 002; Omri Weinstein (ITCS, 2021)

Dynamic influence maximization. - Binghui Peng (NeurIPS, 2021)

Public Good Games in Directed Networks. - Christos H. Papadimitriou; Binghui Peng (CoRR, 2021)

Memory Bounds for Continual Learning. - Xi Chen 001; Christos H. Papadimitriou; Binghui Peng (FOCS, 2022)

Shuffle Private Stochastic Convex Optimization. - Albert Cheu; Matthew Joseph; Jieming Mao; Binghui Peng (ICLR, 2022)

Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions. - Binghui Peng; Andrej Risteski (NeurIPS, 2022)

Computational Hardness of the Hylland-Zeckhauser Scheme. - Thomas Chen; Xi Chen 001; Binghui Peng; Mihalis Yannakakis (SODA, 2022)

On the complexity of dynamic submodular maximization. - Xi Chen 001; Binghui Peng (STOC, 2022)

Dynamic Least-Squares Regression. - Shunhua Jiang; Binghui Peng; Omri Weinstein (CoRR, 2022)

Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization. - Wei Chen 013; Binghui Peng; Grant Schoenebeck; Biaoshuai Tao (J. Artif. Intell. Res., 2022)

Primal-Dual Schemes for Online Matching in Bounded Degree Graphs. - Ilan Reuven Cohen; Binghui Peng (ESA, 2023)

Memory-Query Tradeoffs for Randomized Convex Optimization. - Xi Chen; Binghui Peng (FOCS, 2023)

The Complexity of Dynamic Least-Squares Regression. - Shunhua Jiang; Binghui Peng; Omri Weinstein (FOCS, 2023)

Near Optimal Memory-Regret Tradeoff for Online Learning. - Binghui Peng; Aviad Rubinstein (FOCS, 2023)

Online Prediction in Sub-linear Space. - Binghui Peng; Fred Zhang (SODA, 2023)

Fully-dynamic-to-incremental reductions with known deletion order (e.g. sliding window). - Binghui Peng; Aviad Rubinstein (SOSA, 2023)

Complexity of Equilibria in First-Price Auctions under General Tie-Breaking Rules. - Xi Chen 001; Binghui Peng (STOC, 2023)

Memory-Query Tradeoffs for Randomized Convex Optimization. - Xi Chen 001; Binghui Peng (CoRR, 2023)

Robust Load Balancing with Machine Learned Advice. - Sara Ahmadian; Hossein Esfandiari; Vahab Mirrokni; Binghui Peng (J. Mach. Learn. Res., 2023)

Public goods games in directed networks. - Christos H. Papadimitriou; Binghui Peng (Games Econ. Behav., 2023)

The complexity of non-stationary reinforcement learning. - Binghui Peng; Christos H. Papadimitriou (ALT, 2024)

The sample complexity of multi-distribution learning. - Binghui Peng (COLT, 2024)

The complexity of approximate (coarse) correlated equilibrium for incomplete information games. - Binghui Peng; Aviad Rubinstein (COLT, 2024)

Fast Swap Regret Minimization and Applications to Approximate Correlated Equilibria. - Binghui Peng; Aviad Rubinstein (STOC, 2024)

On Limitations of the Transformer Architecture. - Binghui Peng; Srini Narayanan; Christos H. Papadimitriou (CoRR, 2024)

Theoretical limitations of multi-layer Transformer. - Lijie Chen 001; Binghui Peng; Hongxun Wu (CoRR, 2024)

Learning through the lens of computation - Binghui Peng (2024)

High dimensional online calibration in polynomial time. - Binghui Peng (CoRR, 2025)