Sinkhorn Algorithm

The Sinkhorn algorithm efficiently approximates solutions to optimal transport (OT) problems, which measure the distance between probability distributions, by iteratively scaling matrices until they satisfy specified marginal constraints. Current research focuses on improving the algorithm's speed and robustness through techniques like annealing, debiasing, and incorporating sparsity, as well as extending its application to constrained OT problems and various machine learning tasks such as knowledge tracing, fair ranking, and knowledge distillation. This work is significant because efficient OT computation is crucial for numerous applications across diverse fields, enabling improved performance in areas ranging from image processing and recommendation systems to reinforcement learning and semi-supervised learning.