Distributional Robustness

Distributional robustness in machine learning aims to develop models that perform reliably across diverse data distributions, mitigating the impact of data shifts between training and deployment. Current research focuses on developing algorithms and optimization frameworks, such as those based on Wasserstein distances and minimax formulations, to enhance model robustness against adversarial attacks and subpopulation shifts, often incorporating techniques from Bayesian methods and causal inference. This field is crucial for building trustworthy and reliable AI systems, particularly in high-stakes applications where model performance must be consistent across various demographics and environmental conditions.