Sample Reweighting

Sample reweighting is a technique used to adjust the influence of individual data points during model training, primarily aiming to improve model performance and fairness by addressing issues like data bias, noise, and covariate shift. Current research focuses on developing sophisticated reweighting schemes, often integrated within bilevel optimization frameworks, to dynamically adapt weights based on factors such as sample loss curves, model performance across subgroups, or adherence to physical laws in specific applications (e.g., urban flow prediction). This approach holds significant promise for enhancing the robustness and generalizability of machine learning models across diverse domains, leading to more accurate and equitable predictions in various fields.