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math.PR2022
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…
math.PR2022★ 1 cited
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…