Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.
Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Delves into score-based generative models, exploring learning natural distributions and the impact of neural network architecture on robustness.
