Overlap Bias

Overlap bias, the disproportionate influence of overlapping information between data points or inputs, is a significant challenge across various machine learning domains. Current research focuses on understanding and mitigating this bias in diverse applications, including sequential recommendation systems (analyzing the impact of item position within sequences), point cloud registration (improving accuracy by explicitly modeling overlapping regions), and natural language processing (addressing biases in word recognition and natural language inference models). Addressing overlap bias is crucial for improving the robustness, accuracy, and fairness of machine learning models across numerous fields, from computer vision to information retrieval.