How to Achieve Higher Accuracy with Less Training Points?

In the era of large-scale model training, the extensive use o f available datasets has resulted in significant computation al inefficiencies. T o tackle this issue, we explore methods for identifying informative subsets of training data that can achieve comparable or even superior model performance. W e propose a technique based on influence functions to determine which training samples should be included in the training set. W e conducted empirical evaluations of our method on binary classification tasks utilizing logistic re - gression models. Our approach demonstrates performance comparable to that of training on the entire dataset while using only 10% of the data. Furthermore, we found that our method achieved even higher accuracy when trained with just 60% of the data.