Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
