Lectures related to Decentralized ML: Collaborative Training & Causal Influence Structure

Explores solutions and systems related to privacy in data analysis.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Explores the ubiquity of graphs in modern data and analytics, focusing on the shift in organizations' perception of graph technologies.

Explores neural signals, connectomes, graph theory, and multi-voxel pattern analysis in fMRI trials.

Explores automorphisms of graphs, focusing on automorphism groups, Cayley-Abels graphs, and quasi-isometry.

Explores synthetic data generation for privacy-preserving data publishing, evaluating its effectiveness against privacy threats in raw datasets.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores the properties of undirected and directed graphs, emphasizing connectivity and network topology modeling.