FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
Main Conference Track
Authors: Youguang Chen, George Biros

We investigate theory and algorithms for pool-based active learning for multiclass classification using multinomial logistic regression.  Using finite sample analysis, we prove that the Fisher Information Ratio (FIR)  lower and upper bounds  the excess risk. Based on our theoretical analysis, we propose an active learning algorithm that  employs regret minimization to minimize the FIR. To verify our derived excess risk bounds, we conduct experiments on synthetic datasets. Furthermore, we compare FIRAL with five other methods and found that our scheme  outperforms them: it consistently produces the smallest classification error in the multiclass logistic regression setting, as demonstrated through experiments on MNIST, CIFAR-10, and 50-class ImageNet.