Machine Learning Assumption

Machine learning (ML) model performance critically depends on underlying assumptions about the data, often implicitly assuming data independence and identical distribution (IID). Current research focuses on relaxing these IID assumptions, exploring how data dependencies and distributional shifts impact model accuracy and robustness, particularly within federated learning and neurosymbolic approaches. This involves developing new algorithms and theoretical frameworks to handle non-IID data, including investigating the role of model architecture (e.g., transformers) and leveraging techniques like invariant learning and risk minimization to improve generalization across diverse data distributions. Addressing these assumptions is crucial for building more reliable and adaptable ML systems applicable to real-world scenarios with complex, heterogeneous data.