Papers

Preprints

1. Geometry, Computation, and Optimality in Stochastic Optimization
   C. Cheng, J. Duchi, D. Levy, 2024. [paper]

2. How Many Labelers Do You Have? A Closer Look at Gold-Standard Labels
   C. Cheng, H. Asi, J. Duchi, 2022. [paper]

3. The High-Dimensional Asymptotics of First Order Methods with Random Data
   M. Celentano, C. Cheng, A. Montanari, 2021. [paper]

Conferences

1. Two Fundamental Limits for Uncertainty Quantification in Predictive Inference
   F. Areces, C. Cheng, J. Duchi, 2024, R. Kuditipudi. Conference on Learning Theory (COLT), 2024. [paper]

2. Collaboratively Learning Linear Models with Structured Missing Data
   C. Cheng, G. Cheng, J. Duchi, 2023. Conference on Neural Information Processing Systems (NeurIPS), 2023. [paper]

3. Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression
   C. Cheng, J. Duchi, R. Kuditipudi, 2022. Conference on Learning Theory (COLT), 2022. [paper][slides]

Journals

1. Dimension Free Ridge Regression
   C. Cheng, A. Montanari, 2022. Accepted to Annals of Statistics, 2024. [paper]

2. Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
   S. Cen, C. Cheng, Y. Chen, Y. Wei, Y. Chi, 2020. Operations Research, 2021. [paper][slides]

3. Tackling Small Eigen-gaps: Fine-Grained Eigenvector Estimation and Inference under Heteroscedastic Noise
   C. Cheng, Y. Wei, Y. Chen, 2020. IEEE Transactions on Information Theory, 2021. [paper][slides]

4. Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices
   Y. Chen, C. Cheng, J. Fan, 2018. Annals of Statistics, vol. 49, no. 1, pp. 435-458, 2021. [paper][slides]