Paper "Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning" accepted at ICLR 2024
The paper "Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning“ by Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysis Kalogerias, Nezihe Merve Gürel and Theodoros Rekatsinas has been accepted at external pageICLR 2024.
The paper proposes using Repeated Sampling of Random Subsets (RS2) to achieve faster time-to-accuracy during training. It finds RS2 as being the best performing across thirty state-of-the-art methods for data pruning and data distillation across four datasets.