Batch Acquisition

Batch acquisition focuses on efficiently selecting multiple data points simultaneously for tasks like model training or experimental design, aiming to maximize information gain while minimizing resource consumption. Current research emphasizes developing sophisticated acquisition functions, often employing Bayesian optimization or greedy algorithms, and exploring efficient approximations for large-scale problems, including the use of Gaussian processes and deep learning models. These advancements are crucial for accelerating progress in diverse fields, such as materials science, engineering design, and active learning, by enabling more efficient data collection and analysis in computationally expensive scenarios.