Anytime Valid Sequential Inference

Anytime valid sequential inference focuses on developing methods that provide reliable statistical conclusions at any point during a sequential data analysis, regardless of when the analysis is stopped. Current research emphasizes techniques like e-processes for combining evidence from different data sources, and the development of algorithms for anytime model selection and classification within architectures such as early-exit neural networks and neural ordinary differential equations. This field is crucial for applications requiring real-time decision-making under uncertainty, such as online learning, continual learning, and the monitoring of dynamic systems, offering improved efficiency and robustness compared to traditional batch methods.