Greedy Sampling

Greedy sampling is a family of techniques used to efficiently select subsets of data for various machine learning tasks, particularly when dealing with high-dimensional or computationally expensive datasets. Current research focuses on improving the efficiency and effectiveness of greedy sampling within specific applications, such as Bayesian optimization, multi-objective combinatorial optimization, and sparse identification of nonlinear dynamical systems, often employing novel algorithms that incorporate elements of reinforcement learning or adapt existing methods like Nyström regularization. These advancements are significant because they enable faster and more accurate model training and inference across diverse fields, ranging from materials science and engineering design to financial modeling and time series forecasting.