@article{21504,
  abstract     = {Selecting an appropriate divergence measure is a critical aspect of machine learning, as it directly impacts model performance. Among the most widely used, we find the Kullback–Leibler (KL) divergence, originally introduced in kinetic theory as a measure of relative entropy between probability distributions. Just as in machine learning, the ability to quantify the proximity of probability distributions plays a central role in kinetic theory. In this paper, we present a comparative review of divergence measures rooted in kinetic theory, highlighting their theoretical foundations and exploring their potential applications in machine learning and artificial intelligence.},
  author       = {Auricchio, Gennaro and Brigati, Giovanni and Giudici, Paolo and Toscani, Giuseppe},
  issn         = {1793-6314},
  journal      = {Mathematical Models and Methods in Applied Sciences},
  number       = {6},
  pages        = {1185--1233},
  publisher    = {World Scientific Publishing},
  title        = {{From kinetic theory to AI: A rediscovery of high-dimensional divergences and their properties}},
  doi          = {10.1142/S0218202526410010},
  volume       = {36},
  year         = {2026},
}