We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …

We analyze the correlation between different assets in the cryptocurrency market throughout
different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained …

We employ graph-based methods to examine the connectedness between cryptocurrencies
of different market caps over time. By applying denoising and detrending techniques …

At the core of insurance business lies classification between risky and non-risky insureds,
actuarial fairness meaning that risky insureds should contribute more and pay a higher …

Energy distance is a statistical distance between the distributions of random vectors, which
characterizes equality of distributions. The name energy derives from Newton's gravitational …

Network capacity and reliability for free space optical communication (FSOC) is strongly
driven by ground station availability, which is dominated by local cloud cover causing an …

Estimation of time series models using residuals dependence measures Page 1 The Annals of
Statistics 2022, Vol. 50, No. 5, 3039–3063 https://doi.org/10.1214/22-AOS2220 © Institute of …

In various disciplines, discerning dependencies between variables remains a crucial
undertaking. While correlation measures like Pearson, Spearman, and Kendall provide …

In finite-dimensional, chaotic, Lorenz-like wave-particle dynamical systems one can find
diffusive trajectories, which share their appearance with that of laminar chaotic diffusion …