Welcome

This is my first post here. It’s different from upcoming posts because it was first published on Medium before I opened this blog.

Please feel free to read my first article on medium, follow the link.

Reference

Here you can find some reference I use for my original post.

• “Optimal Transport on Discrete Domains” by Justin Solomon: This paper explores optimal transport theory in the context of discrete domains, providing insights into computational methods and applications.
• “A Short Introduction to Optimal Transport and Wasserstein Distance” by Alex H. Williams: This blog post offers an intuitive overview of optimal transport theory and Wasserstein distance.
• “Optimal Transport and Wasserstein Distance” by Larry Wasserman: This PDF document delves into the mathematical foundations of optimal transport and Wasserstein distance, exploring their applications in statistics and machine learning.
• “Information Geometry Connecting Wasserstein Distance and Kullback-Leibler Divergence via the Entropy-Relaxed Transportation Problem” by Shun-ichi Amari et al.: This research paper presents a unified framework linking Wasserstein distance and KL divergence through entropy-relaxed optimal transport ; Deeper Math.
• “Mapping Between Two Gaussians Using Optimal Transport and the KL Divergence” by Calvin McCarter: This post explores the relationship between optimal transport and KL divergence
• “From GAN to WGAN” by Lilian Weng: This comprehensive post delves into Generative Adversarial Networks (GANs) and introduces the concept of Wasserstein GANs (WGANs).
• Shannon Entropy and Kullback-Leibler Divergence

Citation

Here is the content referencing the work.

for bibtex atribution.

@article{hyr2025distribdistance,
  title   = {The quest for understanding distribution distance : from theory to practice},
  author  = {Hyr, Tncr},
  journal = {Tncr.github.io},
  year    = {2024},
  month   = {Nov},
  url     = {undefined}
}