Machine Learning (stat.ML)

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

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks

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

Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability

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

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP