Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

The quality of training data impacts the performance of pre-trained large language models
(LMs). Given a fixed budget of tokens, we study how to best select data that leads to good …

Not all data in a typical training set help with generalization; some samples can be overly
ambiguous or outrightly mislabeled. This paper introduces a new method to identify such …

Training sample re-weighting is an effective approach for tackling data biases such as
imbalanced and corrupted labels. Recent methods develop learning-based algorithms to …

Performing controlled experiments on noisy data is essential in understanding deep
learning across noise levels. Due to the lack of suitable datasets, previous research has only …

Inspired by human learning, researchers have proposed ordering examples during training
based on their difficulty. Both curriculum learning, exposing a network to easier examples …

Curriculum learning is a technique to improve a model performance and generalization
based on the idea that easy samples should be presented before difficult ones during …

Large scale image classification datasets often contain noisy labels. We take a principled
probabilistic approach to modelling input-dependent, also known as heteroscedastic, label …

Experimental results have shown that curriculum learning, ie, presenting simpler examples
before more complex ones, can improve the efficiency of learning. Some recent theoretical …