Group Wise

"Group-wise" methods in machine learning focus on analyzing and modeling data based on predefined or learned groupings, aiming to improve fairness, robustness, and efficiency across diverse subpopulations or data characteristics. Current research explores algorithms that efficiently handle numerous overlapping groups, employing techniques like group-aware priors, group-wise gradient clipping, and neural network architectures designed for group-invariant functions. These advancements are significant for addressing biases in AI, enhancing the interpretability of models, improving the compression and processing of large datasets, and developing more robust and efficient machine learning systems.