# Prioritized training on points that are learnable, worth learning, and not yet learned

@article{Mindermann2022PrioritizedTO, title={Prioritized training on points that are learnable, worth learning, and not yet learned}, author={S{\"o}ren Mindermann and Muhammed Razzak and Winnie Xu and Andreas Kirsch and Mrinank Sharma and Adrien Morisot and Aidan N. Gomez and Sebastian Farquhar and Janina Brauner and Yarin Gal}, journal={ArXiv}, year={2022}, volume={abs/2206.07137} }

A new conference version of this workshop paper is available at: https://arxiv.org/abs/2206.07137 We introduce Goldilocks Selection , a technique for faster model training which selects a sequence of training points that are “just right”. We propose an information-theoretic acquisition function— the reducible validation loss—and compute it with a small proxy model—GoldiProx—to efﬁciently choose training points that maximize information about the labels of a validation set. We show that the…

## 8 Citations

### Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers

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Distilling knowledge from a large teacher model to a lightweight one is a widely successful approach for generating compact, powerful models in the semi-supervised learning setting where a limited…

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It is shown that averaging the weights of the k latest checkpoints, each collected at the end of an epoch, can speed up the training progression in terms of loss and accuracy by dozens of epochs, corresponding to time savings up to ~ 68 and ~ 30 GPU hours when training a ResNet50 on ImageNet and RoBERTa-Base model on WikiText-103.

### Prioritizing Samples in Reinforcement Learning with Reducible Loss

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An algorithm to prioritize samples with high learn-ability, while assigning lower priority to those that are hard-to-learn, typically caused by noise or stochasticity is developed.

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