Optimization of the Sherrington-Kirkpatrick Hamiltonian
Explore the optimization of the Sherrington-Kirkpatrick Hamiltonian, covering stochastic block models, random graphs, and algorithmic insights for solving complex optimization problems.
Fast Uniform Generation of Random Graphs with Given Degree Sequences
Explore efficient algorithms for generating random graphs with specified degree sequences, focusing on rejection and switching methods.
Interacting Stochastic Processes on Random Graphs
Explore interacting stochastic processes on random graphs, covering models like voter and neuronal hox, and examining challenges in sparse regimes and local convergence.
The Largest Common Subtree of Uniform Attachment Trees
Explore the largest common subtree of uniform attachment trees in this advanced mathematics workshop, featuring expert talks and collaborative sessions on network analysis and discrete probability.
Statistical Network Analysis - Course 2
Explore statistical network analysis techniques with experts from Texas A&M and Wisconsin. Gain insights into discrete probability, collaborate on open problems, and advance your research in this month-long program.
Statistical Network Analysis - Course 2
Explore statistical network analysis techniques with experts from Texas A&M and Wisconsin. Gain insights into discrete probability, collaborate on open problems, and advance your research in this month-long program.
Statistical Network Analysis - Course 2
Explore statistical network analysis techniques with experts from Texas A&M and Wisconsin. Gain insights into discrete probability, collaborate on open problems, and advance your research in this month-long program.
Threshold for a Random Graph to Contain a Triangle - MIT 18.226
Explore probabilistic methods to determine when random graphs develop triangles, using first and second moment analysis techniques in combinatorial mathematics.
Bounded Differences Inequality and Azuma-Hoeffding Inequality
Master probabilistic analysis through the Bounded Differences Inequality, exploring its applications in coupon collection and random graph coloring, with focus on Lipschitz functions and concentration bounds.