Importance Weight

Importance weighting is a technique used to adjust the influence of data points in machine learning models, primarily addressing issues arising from data imbalance or distribution shifts. Current research focuses on developing improved importance weighting methods, particularly within the context of off-policy evaluation and reinforcement learning, and exploring their application in diverse areas such as adversarial training, and subset selection. These advancements aim to enhance model robustness, accuracy, and efficiency in various applications, including recommender systems and large language models, by mitigating the negative effects of skewed data distributions.