Distribution Change

Distribution change, encompassing shifts in data distributions over time or across different datasets, is a central challenge in many machine learning applications. Current research focuses on developing robust methods for detecting these shifts, including novel cumulative sum procedures and invariance-based decompositions, as well as adapting models to mitigate their negative impact on performance. This involves exploring techniques like distributional transformations, probability shift adjustments in neural architecture search, and region-based performance evaluations to improve model robustness and accuracy in non-stationary environments. Addressing distribution change is crucial for building reliable and adaptable machine learning systems across diverse applications, from time series forecasting to federated learning.