Dependent Data

Dependent data, where observations are not independent and identically distributed, poses significant challenges for traditional machine learning methods. Current research focuses on developing theoretical frameworks and algorithms that account for various forms of dependence, including temporal dependencies in time series and spatial dependencies in graph-structured data, often employing techniques like kernel methods, graph neural networks, and modifications to stochastic gradient descent. This work is crucial for improving the accuracy and reliability of machine learning models in numerous real-world applications where the i.i.d. assumption is violated, such as in time series forecasting, network analysis, and scientific modeling. Addressing the challenges of dependent data is essential for advancing the field and enabling more robust and reliable AI systems.