Stochastic Block ModelCovers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Generalized Linear ModelsExplores Generalized Linear Models, Bayesian methods, compressed sensing, and perception in high-dimensional statistics.
Graph Coloring IIExplores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Iterative Algorithms: GAMPCovers the GAMP algorithm for iterative signal reconstruction and introduces proximal gradient descent for L1 minimization problems.
Applications of GAMPDelves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.
Statistical Physics of LearningOffers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
Linear Models: LASSO and AMPCovers linear problems, LASSO, and AMP in supervised learning, including Generalized Linear Models and N-dimensional models.
Maximum of GaussiansExplores the p-spin model in spin glass theory and the convergence to the Random Energy Model using Gaussian integrals and the replica method.
Replica for the p-spin modelExplores the computation of the replica for the p-spin model, focusing on decoupling and recoupling replicas to simplify calculations.
