Future Data

Future data research focuses on anticipating and adapting to evolving data distributions in various machine learning contexts. Current efforts concentrate on generating synthetic future data using generative models, particularly for continual learning and class-incremental learning scenarios, and employing hypernetworks to preemptively adjust model parameters for time series forecasting. This work is crucial for improving the robustness and adaptability of machine learning models in dynamic environments, leading to more reliable predictions and more efficient handling of streaming data in applications ranging from time series forecasting to federated learning.