Feb. 13, 2024, 5:42 a.m. | Rudrajit Das Xi Chen Bertram Ieong Parikshit Bansal Sujay Sanghavi

cs.LG updates on arXiv.org arxiv.org

It is well known that selecting samples with large losses/gradients can significantly reduce the number of training steps. However, the selection overhead is often too high to yield any meaningful gains in terms of overall training time. In this work, we focus on the greedy approach of selecting samples with large \textit{approximate losses} instead of exact losses in order to reduce the selection overhead. For smooth convex losses, we show that such a greedy strategy can converge to a constant …

cs.lg focus losses reduce samples sampling stat.ml terms training understanding work

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