Optimal Transport: Theory and Applications

Related lectures (31)

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Explains how entropy measures uncertainty in a system based on possible outcomes.

Explores variational formulation for measuring information content and divergence between probability distributions.

Discusses conditional entropy and its role in information theory and data compression.

Explores convex sets, their properties, and applications in optimization.

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.

Explores optimal transport theory with a focus on heat equations and Wasserstein distance.

Explores infimum, supremum, and absolute value concepts with examples and proofs.

Explores the theory of optimal transport, focusing on Lipschitz functions and uniqueness of solutions.

Explores Lagrange multipliers, minimax theorems, and convex subsets in optimal transport theory.

Explores Coxeter groups, word length, simple reflections, and unique elements.