2024
On the Convergence of Loss and Uncertainty-based Active Learning Algorithms
NeurIPS 2024poster
We investigate the convergence rates and data sample sizes required for training a machine learning model using a stochastic gradient descent (SGD) algorithm, where data points are sampled based on either their loss value or uncertainty value. These training methods are particularly relevant for act…