This paper surveys some recent developments in measures of association related to a new
coefficient of correlation introduced by the author. A straightforward extension of this …

We study fair machine learning (ML) under predictive uncertainty to enable reliable and
trustworthy decision-making. The seminal work of'equalized coverage'proposed an …

Minimax optimality of permutation tests Page 1 The Annals of Statistics 2022, Vol. 50, No. 1,
225–251 https://doi.org/10.1214/21-AOS2103 © Institute of Mathematical Statistics, 2022 …

Local permutation tests for conditional independence Page 1 The Annals of Statistics 2022, Vol.
50, No. 6, 3388–3414 https://doi.org/10.1214/22-AOS2233 © Institute of Mathematical Statistics …

We propose and study a class of simple, nonparametric, yet interpretable measures of
conditional dependence, which we call kernel partial correlation (KPC) coefficient, between …

The projected covariance measure for assumption-lean variable significance testing Page 1
The Annals of Statistics 2024, Vol. 52, No. 6, 2851–2878 https://doi.org/10.1214/24-AOS2447 …

Conditional independence (CI) testing is a fundamental and challenging task in modern
statistics and machine learning. Many modern methods for CI testing rely on powerful …

We provide finite sample guarantees for the classical Chow-Liu algorithm (IEEE Trans.
Inform. Theory, 1968) to learn a tree-structured graphical model of a distribution. For a …

On Azadkia…Chatterjee's conditional dependence coefficient Page 1 Bernoulli 30(2), 2024,
851–877 https://doi.org/10.3150/22-BEJ1529 On Azadkia–Chatterjee’s conditional dependence …

We study the problem of testing discrete distributions with a focus on the high probability
regime. Specifically, given samples from one or more discrete distributions, a property P …