Delves into instrumental variable approaches for optimal treatment regimes, including precision medicine and personalized recommendations, with a focus on identification conditions and robust classification-based estimators.
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Emphasizes the importance of managing trade-offs for product robustness in mechanical design, using Multi-objective Monotonicity Analysis for quantitative analysis and systematic redesign efforts.
Explores the challenges of robust vision, including distribution shifts, failure examples, and strategies for improving model robustness through diverse data pretraining.
