88 citations · 114 across the 24 of their papers we have counts for
4 papers · 1 filter
Wasserstein-p Bounds in the Central Limit Theorem Under Weak Dependence
Tianle Liu, Morgane Austern
The central limit theorem is one of the most fundamental results in probability and has been successfully extended to locally dependent data and strongly-mixing random fields. In t…
Efficient Concentration with Gaussian Approximation
Morgane Austern, Lester Mackey
Concentration inequalities for the sample mean, like those due to Bernstein, Hoeffding, and Bentkus, are valid for any sample size but overly conservative, yielding confidence inte…
Debiased Machine Learning without Sample-Splitting for Stable Estimators
Qizhao Chen, Vasilis Syrgkanis, Morgane Austern
Estimation and inference on causal parameters is typically reduced to a generalized method of moments problem, which involves auxiliary functions that correspond to solutions to a…
Gaussian and Non-Gaussian Universality of Data Augmentation
Kevin Han Huang, Peter Orbanz, Morgane Austern
We provide universality results that quantify how data augmentation affects the variance and limiting distribution of estimates through simple surrogates, and analyze several speci…