Performance Gap

Performance gaps, discrepancies in model accuracy across different subgroups or tasks, are a central concern in machine learning. Current research focuses on identifying and mitigating these gaps, exploring techniques like slice discovery methods to pinpoint causes of bias in medical image analysis, and analyzing the impact of data characteristics (e.g., size, noise, label quality) and training strategies (e.g., online vs. offline, parameter-efficient fine-tuning) on model performance across various architectures (e.g., transformers, convolutional neural networks). Understanding and addressing these performance gaps is crucial for improving the fairness, reliability, and generalizability of machine learning models, with implications for diverse fields including healthcare, natural language processing, and computer vision.