Model Agnostic

Model-agnostic methods in machine learning aim to develop techniques applicable across diverse model architectures, improving generalizability and reducing reliance on specific algorithms. Current research focuses on enhancing explainability, uncertainty quantification, and efficient resource utilization across various applications, including image generation, robotics, and natural language processing, often employing techniques like surrogate models, counterfactual analysis, and feature manipulation. This approach is significant because it promotes broader applicability, facilitates comparisons between different models, and ultimately leads to more robust and trustworthy AI systems across a wider range of domains.