Statistics Theory (math.ST)

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

Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients

Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits

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

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands

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