Distribution Free

Distribution-free methods in machine learning aim to provide reliable predictions and uncertainty quantification without making assumptions about the underlying data distribution. Current research focuses on developing and refining conformal prediction methods, often combined with techniques like quantile regression and graph neural networks, to achieve valid coverage guarantees even with complex data structures and temporal drifts. This approach is crucial for ensuring the robustness and reliability of machine learning models in high-stakes applications, particularly where distributional assumptions are unrealistic or unverifiable, such as in federated learning and fairness-aware algorithms. The resulting distribution-free guarantees enhance the trustworthiness and applicability of machine learning across diverse domains.