Curriculum Learning is motivated by human cognition, where teaching often involves
gradually exposing the learner to examples in a meaningful order, from easy to hard …

We provide theoretical investigation of curriculum learning in the context of stochastic
gradient descent when optimizing the convex linear regression loss. We prove that the rate …

Curriculum learning (CL) is a commonly used machine learning training strategy. However,
we still lack a clear theoretical understanding of CL's benefits. In this paper, we study the …

Not all instances in a data set are equally beneficial for inferring a model of the data, and
some instances (such as outliers) can be detrimental. Several machine learning techniques …

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 …

Humans and animals learn much better when the examples are not randomly presented but
organized in a meaningful order which illustrates gradually more concepts, and gradually …

Curriculum learning (CL)-training using samples that are generated and presented in a
meaningful order-was introduced in the machine learning context around a decade ago …

We study a novel curriculum learning scheme where in each round, samples are selected to
achieve the greatest progress and fastest learning speed towards the ground-truth on all …

Creating curriculum with an ever-changing student body is difficult. Faculty members in a
given department will have different perspectives on the composition and academic needs …

We introduce and study minimax curriculum learning (MCL), a new method for adaptively
selecting a sequence of training subsets for a succession of stages in machine learning. The …