Gaussianized Design Optimization for Covariate Balance in Randomized Experiments
This paper presents Gaussianized Design Optimization, a novel framework for optimally balancing covariates in experimental design.
JRSSB
Universal Prediction Band via Semi-Definite Programming
This paper proposes a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification.
Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients
Modern statistical inference tasks often require iterative optimization methods to compute the solution. Convergence analysis from an optimization viewpoint only informs us how well the solution is approximated numerically but overlooks the sampling nature of the data. We introduce the moment-adjusted stochastic gradient descents, a new stochastic optimization method for statistical inference.