Signaling in Quasipolynomial time. - Yu Cheng 002; Ho Yee Cheung; Shaddin Dughmi; Shang-Hua Teng (CoRR, 2014)

Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models. - Dehua Cheng; Yu Cheng 002; Yan Liu 002; Richard Peng; Shang-Hua Teng (CoRR, 2014)

Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification. - Dehua Cheng; Yu Cheng 002; Yan Liu 002; Richard Peng; Shang-Hua Teng (COLT, 2015)

Mixture Selection, Mechanism Design, and Signaling. - Yu Cheng 002; Ho Yee Cheung; Shaddin Dughmi; Ehsan Emamjomeh-Zadeh; Li Han; Shang-Hua Teng (FOCS, 2015)

Near-Optimal Hardness Results for Signaling in Bayesian Games. - Umang Bhaskar; Yu Cheng 002; Young Kun-Ko; Chaitanya Swamy (CoRR, 2015)

Well-Supported versus Approximate Nash Equilibria: Query Complexity of Large Games. - Xi Chen 001; Yu Cheng 002; Bo Tang (CoRR, 2015)

Spectral Sparsification of Random-Walk Matrix Polynomials. - Dehua Cheng; Yu Cheng 002; Yan Liu 002; Richard Peng; Shang-Hua Teng (CoRR, 2015)

On the Recursive Teaching Dimension of VC Classes. - Xi Chen 001; Yu Cheng 002; Bo Tang (NIPS, 2016)

Hardness Results for Signaling in Bayesian Zero-Sum and Network Routing Games. - Umang Bhaskar; Yu Cheng 002; Young Kun-Ko; Chaitanya Swamy (EC, 2016)

A Note on Teaching for VC Classes. - Xi Chen 001; Yu Cheng 002; Bo Tang (Electron. Colloquium Comput. Complex., 2016)

Well-Supported vs. Approximate Nash Equilibria: Query Complexity of Large Games. - Xi Chen 001; Yu Cheng 002; Bo Tang (ITCS, 2017)

Of the People: Voting Is More Effective with Representative Candidates. - Yu Cheng 002; Shaddin Dughmi; David Kempe 001 (EC, 2017)

Playing Anonymous Games using Simple Strategies. - Yu Cheng 002; Ilias Diakonikolas; Alistair Stewart (SODA, 2017)

On the Distortion of Voting With Multiple Representative Candidates. - Yu Cheng 002; Shaddin Dughmi; David Kempe 001 (AAAI, 2018)

Non-Convex Matrix Completion Against a Semi-Random Adversary. - Yu Cheng 002; Rong Ge 001 (COLT, 2018)

A Deterministic Protocol for Sequential Asymptotic Learning. - Yu Cheng 002; Wade Hann-Caruthers; Omer Tamuz (ISIT, 2018)

Robust Learning of Fixed-Structure Bayesian Networks. - Yu Cheng 002; Ilias Diakonikolas; Daniel Kane 001; Alistair Stewart (NeurIPS, 2018)

A Better Algorithm for Societal Tradeoffs. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (AAAI, 2019)

Faster Algorithms for High-Dimensional Robust Covariance Estimation. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; David P. Woodruff (COLT, 2019)

When Samples Are Strategically Selected. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (ICML, 2019)

Distinguishing Distributions When Samples Are Strategically Transformed. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (NeurIPS, 2019)

High-Dimensional Robust Mean Estimation in Nearly-Linear Time. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001 (SODA, 2019)

High-dimensional Robust Mean Estimation via Gradient Descent. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; Mahdi Soltanolkotabi (ICML, 2020)

Group Fairness in Committee Selection. - Yu Cheng 002; Zhihao Jiang; Kamesh Munagala; Kangning Wang 001 (ACM Trans. Economics and Comput., 2020)

Automated Mechanism Design for Classification with Partial Verification. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (AAAI, 2021)

Classification with Few Tests through Self-Selection. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (AAAI, 2021)

Fair for All: Best-effort Fairness Guarantees for Classification. - Anilesh Kollagunta Krishnaswamy; Zhihao Jiang; Kangning Wang 001; Yu Cheng 002; Kamesh Munagala (AISTATS, 2021)

Sparsification of Directed Graphs via Cut Balance. - Ruoxu Cen; Yu Cheng 002; Debmalya Panigrahi; Kevin Sun 001 (ICALP, 2021)

Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time. - Yu Cheng 002; Honghao Lin (ICLR, 2021)

A Simple Mechanism for a Budget-Constrained Buyer. - Yu Cheng 002; Nick Gravin; Kamesh Munagala; Kangning Wang 001 (ACM Trans. Economics and Comput., 2021)

Planning with Participation Constraints. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (AAAI, 2022)

Outlier-Robust Sparse Estimation via Non-Convex Optimization. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; Shivam Gupta 002; Daniel Kane 001; Mahdi Soltanolkotabi (NeurIPS, 2022)

Efficient Algorithms for Planning with Participation Constraints. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (EC, 2022)

Hiding Data Helps: On the Benefits of Masking for Sparse Coding. - Muthu Chidambaram; Chenwei Wu 002; Yu Cheng 002; Rong Ge 001 (ICML, 2023)

Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. - Shuyao Li 001; Yu Cheng 002; Ilias Diakonikolas; Jelena Diakonikolas; Rong Ge 001; Stephen J. Wright 001 (NeurIPS, 2023)

Efficiently Solving Turn-Taking Stochastic Games with Extensive-Form Correlation. - Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (EC, 2023)

Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction. - Yixuan Xu; Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (NeurIPS, 2024)

Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction. - Yixuan Even Xu; Hanrui Zhang 001; Yu Cheng 002; Vincent Conitzer (CoRR, 2024)

Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut. - Yu Cheng 002; Max Li; Honghao Lin; Zi-Yi Tai; David P. Woodruff; Jason Zhang (Proc. ACM Manag. Data, 2024)

A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook. - Jiankai Sun; Chuanyang Zheng; Enze Xie; Zhengying Liu; Ruihang Chu; Jianing Qiu; Jiaqi Xu; Mingyu Ding; Hongyang Li 001; Mengzhe Geng; Yue Wu 012; Wenhai Wang; et al. (ACM Comput. Surv., 2025)