Explore a seminar on the blessings of dimensionality for Gaussian latent factor models presented by Greg Ver Steeg from the University of Southern California. Delve into the challenges of learning graphical model structures from high-dimensional data and discover how restricting overlaps among latent factors can transform the curse of dimensionality into a blessing. Examine theoretical results suggesting that sample complexity can decrease with dimensionality under certain conditions. Learn about a novel method that leverages this blessing for high-dimensional structure recovery using limited samples. Investigate the practical applications of this approach through case studies on under-sampled data from brain fMRI and financial markets. Gain insights into topics such as mutual information, graphical models, information theory, latent remodeling, conditional independence, and unsupervised dimensionality reduction. Understand the implications for improving reproducibility in neuroscience and other fields dealing with high-dimensional data analysis. Read more