arXiv:2603.01346v2 Announce Type: replace Abstract: We revisit the framework of Smart PAC learning, which seeks supervised learners which compete with semi-supervised learners that are provided full knowledge of the marginal distribution on unlabeled data. Prior work has shown that such marginal-by-marginal guarantees are possible for "most" marginals, with respect to an arbitrary fixed and known measure, but not more generally. We discover that this failure can be attributed to an "indistinguishability" phenomenon: There are marginals which cannot be statistically distinguished from other mar
This paper re-evaluates fundamental assumptions in machine learning, offering a new theoretical framework for instance-optimal learning, potentially driven by the increasing complexity and scale of real-world AI applications.
Improved theoretical understanding of PAC learning can lead to more robust, efficient, and less data-intensive AI systems, making current models smarter and more adaptable with less supervised data.
The proposed 'Relatively Smart' approach suggests a path toward learning algorithms that are less reliant on exhaustive marginal distribution knowledge, potentially opening new avenues for semi-supervised and unsupervised learning.
- · AI researchers
- · Machine learning startups
- · Data-scarce industries
- · Companies reliant on massive amounts of labeled data
Refined theoretical understanding of AI learning paradigms.
Development of new algorithms that require less explicit supervision and compete more effectively with semi-supervised methods.
Acceleration of AI deployment in domains where labeled data is scarce or expensive, increasing AI's ubiquity and efficiency.
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Read at arXiv cs.LG