research
∙
07/09/2023
Wasserstein-p Bounds in the Central Limit Theorem Under Local Dependence
The central limit theorem (CLT) is one of the most fundamental results i...
share
research
∙
03/08/2023
Inference on Optimal Dynamic Policies via Softmax Approximation
Estimating optimal dynamic policies from offline data is a fundamental p...
research
∙
09/19/2022
Wasserstein-p Bounds in the Central Limit Theorem under Weak Dependence
The central limit theorem is one of the most fundamental results in prob...
research
∙
08/21/2022
Efficient Concentration with Gaussian Approximation
Concentration inequalities for the sample mean, like those due to Bernst...
research
∙
06/03/2022
Debiased Machine Learning without Sample-Splitting for Stable Estimators
Estimation and inference on causal parameters is typically reduced to a ...
research
∙
02/18/2022
Quantifying the Effects of Data Augmentation
We provide results that exactly quantify how data augmentation affects t...
research
∙
07/06/2021
Asymptotics of Network Embeddings Learned via Subsampling
Network data are ubiquitous in modern machine learning, with tasks of in...
research
∙
11/23/2020
Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality
One of the most commonly used methods for forming confidence intervals f...
research
∙
01/29/2020
Asymptotics of Cross-Validation
Cross validation is a central tool in evaluating the performance of mach...
research
∙
06/27/2018
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Empirical risk minimization is the principal tool for prediction problem...
research
∙
06/27/2018
Limit theorems for invariant distributions
We consider random processes whose distribution satisfies a symmetry pro...
research
∙
04/16/2018