Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores fundamental groups, homotopy classes, and coverings in connected manifolds.
Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.
Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
Explores the Exponential Family distribution, covering entropy, energy, and moments.
Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.
