Distributional Assumption

Distributional assumptions in machine learning and related fields concern the underlying probability distributions of data, impacting model performance and reliability. Current research focuses on mitigating the negative effects of biased or misspecified distributions, employing techniques like Wasserstein distance-based robustness, importance reweighting for bias correction, and distributional counterfactual explanations for improved interpretability. These advancements are crucial for building more robust and reliable models, particularly in applications where data scarcity, adversarial attacks, or evolving distributions are prevalent, leading to improved accuracy and trustworthiness in diverse fields.