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2020 – today
- 2024
[c32]Binghui Peng, Christos H. Papadimitriou:
The complexity of non-stationary reinforcement learning. ALT 2024: 972-996- [c31]Binghui Peng, Aviad Rubinstein:
The complexity of approximate (coarse) correlated equilibrium for incomplete information games. COLT 2024: 4158-4184 - [c30]Binghui Peng:
The sample complexity of multi-distribution learning. COLT 2024: 4185-4204 - [c29]Binghui Peng
, Aviad Rubinstein:
Fast Swap Regret Minimization and Applications to Approximate Correlated Equilibria. STOC 2024: 1223-1234 - [i29]Binghui Peng, Srini Narayanan, Christos H. Papadimitriou:
On Limitations of the Transformer Architecture. CoRR abs/2402.08164 (2024) - [i28]Binghui Peng, Aviad Rubinstein:
The complexity of approximate (coarse) correlated equilibrium for incomplete information games. CoRR abs/2406.02357 (2024) - 2023
- [j3]Christos H. Papadimitriou, Binghui Peng:
Public goods games in directed networks. Games Econ. Behav. 139: 161-179 (2023) - [j2]Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng:
Robust Load Balancing with Machine Learned Advice. J. Mach. Learn. Res. 24: 44:1-44:46 (2023) - [c28]Ilan Reuven Cohen, Binghui Peng:
Primal-Dual Schemes for Online Matching in Bounded Degree Graphs. ESA 2023: 35:1-35:17 - [c27]Binghui Peng, Aviad Rubinstein:
Near Optimal Memory-Regret Tradeoff for Online Learning. FOCS 2023: 1171-1194 - [c26]Xi Chen, Binghui Peng:
Memory-Query Tradeoffs for Randomized Convex Optimization. FOCS 2023: 1400-1413 - [c25]Shunhua Jiang, Binghui Peng, Omri Weinstein:
The Complexity of Dynamic Least-Squares Regression. FOCS 2023: 1605-1627 - [c24]Binghui Peng, Fred Zhang:
Online Prediction in Sub-linear Space. SODA 2023: 1611-1634 - [c23]Binghui Peng, Aviad Rubinstein:
Fully-dynamic-to-incremental reductions with known deletion order (e.g. sliding window). SOSA 2023: 261-271 - [c22]Xi Chen, Binghui Peng:
Complexity of Equilibria in First-Price Auctions under General Tie-Breaking Rules. STOC 2023: 698-709 - [i27]Binghui Peng, Aviad Rubinstein:
Near Optimal Memory-Regret Tradeoff for Online Learning. CoRR abs/2303.01673 (2023) - [i26]Xi Chen, Binghui Peng:
Complexity of Equilibria in First-Price Auctions under General Tie-Breaking Rules. CoRR abs/2303.16388 (2023) - [i25]Xi Chen, Binghui Peng:
Memory-Query Tradeoffs for Randomized Convex Optimization. CoRR abs/2306.12534 (2023) - [i24]Christos H. Papadimitriou, Binghui Peng:
The complexity of non-stationary reinforcement learning. CoRR abs/2307.06877 (2023) - [i23]Binghui Peng, Aviad Rubinstein:
Fast swap regret minimization and applications to approximate correlated equilibria. CoRR abs/2310.19647 (2023) - [i22]Binghui Peng:
The sample complexity of multi-distribution learning. CoRR abs/2312.04027 (2023) - 2022
- [j1]Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao:
Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization. J. Artif. Intell. Res. 74: 303-351 (2022) - [c21]Xi Chen, Christos H. Papadimitriou, Binghui Peng:
Memory Bounds for Continual Learning. FOCS 2022: 519-530 - [c20]Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng:
Shuffle Private Stochastic Convex Optimization. ICLR 2022 - [c19]Binghui Peng, Andrej Risteski:
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions. NeurIPS 2022 - [c18]Sara Ahmadian, Hossein Esfandiari, Vahab S. Mirrokni, Binghui Peng:
Robust Load Balancing with Machine Learned Advice. SODA 2022: 20-34 - [c17]Thomas Chen, Xi Chen, Binghui Peng, Mihalis Yannakakis:
Computational Hardness of the Hylland-Zeckhauser Scheme. SODA 2022: 2253-2268 - [c16]Xi Chen, Binghui Peng:
On the complexity of dynamic submodular maximization. STOC 2022: 1685-1698 - [i21]Shunhua Jiang, Binghui Peng, Omri Weinstein:
Dynamic Least-Squares Regression. CoRR abs/2201.00228 (2022) - [i20]Binghui Peng, Andrej Risteski:
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions. CoRR abs/2203.14383 (2022) - [i19]Xi Chen, Christos H. Papadimitriou, Binghui Peng:
Memory Bounds for Continual Learning. CoRR abs/2204.10830 (2022) - [i18]Binghui Peng, Fred Zhang:
Online Prediction in Sub-linear Space. CoRR abs/2207.07974 (2022) - [i17]Binghui Peng, Aviad Rubinstein:
Fully-dynamic-to-incremental reductions with known deletion order (e.g. sliding window). CoRR abs/2211.05178 (2022) - 2021
- [c15]Shunyu Yao, Binghui Peng, Christos H. Papadimitriou, Karthik Narasimhan:
Self-Attention Networks Can Process Bounded Hierarchical Languages. ACL/IJCNLP (1) 2021: 3770-3785 - [c14]Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, Christopher Ré:
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. ICLR 2021 - [c13]Jan van den Brand, Binghui Peng, Zhao Song, Omri Weinstein:
Training (Overparametrized) Neural Networks in Near-Linear Time. ITCS 2021: 63:1-63:15 - [c12]Sara Ahmadian, Allen Liu, Binghui Peng, Morteza Zadimoghaddam:
Distributed Load Balancing: A New Framework and Improved Guarantees. ITCS 2021: 79:1-79:20 - [c11]Binghui Peng:
Dynamic influence maximization. NeurIPS 2021: 10718-10731 - [c10]Christos H. Papadimitriou, Binghui Peng:
Public Goods Games in Directed Networks. EC 2021: 745-762 - [i16]Shunyu Yao, Binghui Peng, Christos H. Papadimitriou, Karthik Narasimhan:
Self-Attention Networks Can Process Bounded Hierarchical Languages. CoRR abs/2105.11115 (2021) - [i15]Christos H. Papadimitriou, Binghui Peng:
Public Good Games in Directed Networks. CoRR abs/2106.00718 (2021) - [i14]Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng:
Shuffle Private Stochastic Convex Optimization. CoRR abs/2106.09805 (2021) - [i13]Thomas Chen, Xi Chen, Binghui Peng, Mihalis Yannakakis:
Computational Hardness of the Hylland-Zeckhauser Scheme. CoRR abs/2107.05746 (2021) - [i12]Binghui Peng:
Dynamic Influence Maximization. CoRR abs/2110.12602 (2021) - [i11]Xi Chen, Binghui Peng:
On the Complexity of Dynamic Submodular Maximization. CoRR abs/2111.03198 (2021) - 2020
- [c9]Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao:
Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization. AAAI 2020: 590-597 - [c8]Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, Pingzhong Tang:
Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions. AAAI 2020: 2236-2243 - [c7]Xi Chen, Binghui Peng:
Hedging in games: Faster convergence of external and swap regrets. NeurIPS 2020 - [i10]Xi Chen, Binghui Peng:
Hedging in games: Faster convergence of external and swap regrets. CoRR abs/2006.04953 (2020) - [i9]Jan van den Brand, Binghui Peng, Zhao Song, Omri Weinstein:
Training (Overparametrized) Neural Networks in Near-Linear Time. CoRR abs/2006.11648 (2020) - [i8]Xiaoxiao Li, Yangsibo Huang, Binghui Peng, Zhao Song, Kai Li:
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery. CoRR abs/2010.11463 (2020)
2010 – 2019
- 2019
- [c6]Binghui Peng, Weiran Shen, Pingzhong Tang, Song Zuo:
Learning Optimal Strategies to Commit To. AAAI 2019: 2149-2156 - [c5]Ilan Reuven Cohen, Binghui Peng, David Wajc:
Tight Bounds for Online Edge Coloring. FOCS 2019: 1-25 - [c4]Anupam Gupta, Guru Guruganesh, Binghui Peng, David Wajc:
Stochastic Online Metric Matching. ICALP 2019: 67:1-67:14 - [c3]Wei Chen, Binghui Peng:
On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback. ISAAC 2019: 24:1-24:19 - [c2]Binghui Peng, Wei Chen:
Adaptive Influence Maximization with Myopic Feedback. NeurIPS 2019: 5575-5584 - [c1]Zhiyi Huang, Binghui Peng, Zhihao Gavin Tang, Runzhou Tao, Xiaowei Wu, Yuhao Zhang:
Tight Competitive Ratios of Classic Matching Algorithms in the Fully Online Model. SODA 2019: 2875-2886 - [i7]Ilan Reuven Cohen, Binghui Peng, David Wajc:
Tight Bounds for Online Edge Coloring. CoRR abs/1904.09222 (2019) - [i6]Anupam Gupta, Guru Guruganesh, Binghui Peng, David Wajc:
Stochastic Online Metric Matching. CoRR abs/1904.09284 (2019) - [i5]Binghui Peng, Wei Chen:
Adaptive Influence Maximization with Myopic Feedback. CoRR abs/1905.11663 (2019) - [i4]Wei Chen, Binghui Peng:
On Adaptivity Gaps of Influence Maximization under the Independent Cascade Model with Full Adoption Feedback. CoRR abs/1907.01707 (2019) - [i3]Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao:
Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization. CoRR abs/1911.08164 (2019) - 2018
- [i2]Zhiyi Huang, Binghui Peng, Zhihao Gavin Tang, Runzhou Tao, Xiaowei Wu, Yuhao Zhang:
Tight Competitive Ratios of Classic Matching Algorithms in the Fully Online Model. CoRR abs/1810.07903 (2018) - 2017
- [i1]Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, Pingzhong Tang:
Reinforcement Mechanism Design, with Applications to Dynamic Pricing in Sponsored Search Auctions. CoRR abs/1711.10279 (2017)
Coauthor Index
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last updated on 2024-09-13 00:45 CEST by the dblp team
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