arXiv:2606.11149v1 Announce Type: new Abstract: We study the problem of learning a drifting concept in the presence of Massart noise. In this framework, an online learner has access to a history of independent samples whose labels are noisy versions of a target concept that may change from round to round. The goal is to output, in each round, a hypothesis with small prediction error. We study the complexity of this learning problem for the fundamental class of margin-separable linear classifiers (halfspaces). On the positive side, we give a computationally efficient learner achieving error $\e
This academic paper was recently published on arXiv, contributing to ongoing research in machine learning theory.
This item is important for specialists in machine learning theory as it addresses efficient learning under noise, but has no immediate or strategic relevance for a broader audience.
Nothing immediately changes outside of the academic understanding of specific learning algorithms.
Further theoretical development in robust machine learning algorithms.
Potential for improved algorithmic efficiency in niche AI applications, many years into the future.
No discernible third-order consequence for strategic planning.
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