dataadaptivity
Data adaptivity is the capability of a computational system to modify its processing strategy in response to properties of the input data. It involves adjusting aspects such as sampling, model complexity, feature processing, or algorithmic hyperparameters based on data characteristics like distribution, noise, sparsity, or available resources. The aim is to improve accuracy, efficiency, or robustness while controlling computational cost.
In machine learning and statistics, data adaptivity appears in online and incremental learning, where models update
Implementation approaches include adaptive sampling, where the data collection strategy changes with observed information; data-driven hyperparameter
Challenges include avoiding overfitting to recent data, ensuring stability and reproducibility, balancing exploration and exploitation in