1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Bandit-Driven Batch Selection for Robust Learning under Label Noise
Michal Lisicki, Mihai Nica, Graham W. Taylor
We introduce a novel approach for batch selection in Stochastic Gradient Descent (SGD) training, leveraging combinatorial bandit algorithms. Our methodology focuses on optimizing t…
cs.LG2022★ 1 cited
Bounding generalization error with input compression: An empirical study with infinite-width networks
Angus Galloway, Anna Golubeva, Mahmoud Salem +3
Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data. The ability to better predict GE ba…