Statistics Theory (math.ST)

On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results

Just Interpolate: Kernel ''Ridgeless'' Regression Can Generalize

Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information

Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix

On Detection and Structural Reconstruction of Small-World Random Networks

Geometric Inference for General High-Dimensional Linear Inverse Problems

Learning with Square Loss: Localization through Offset Rademacher Complexity

Law of Log Determinant of Sample Covariance Matrix and Optimal Estimation of Differential Entropy for High-Dimensional Gaussian Distributions