Coresets for Data-efficient Training of Machine Learning Models
This addresses the data efficiency challenge in training machine learning models, offering a rigorous method for reducing computational costs without sacrificing performance, which is incremental but provides strong practical gains.
The paper tackles the problem of selecting a training data subset for efficient machine learning by developing CRAIG, a method that uses coresets to approximate the full gradient, achieving speedups of up to 6x for logistic regression and 3x for deep neural networks while maintaining similar solution quality.
Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can theoretically and practically perform on par with the full dataset. Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function. We prove that applying IG to this subset is guaranteed to converge to the (near)optimal solution with the same convergence rate as that of IG for convex optimization. As a result, CRAIG achieves a speedup that is inversely proportional to the size of the subset. To our knowledge, this is the first rigorous method for data-efficient training of general machine learning models. Our extensive set of experiments show that CRAIG, while achieving practically the same solution, speeds up various IG methods by up to 6x for logistic regression and 3x for training deep neural networks.