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New framework establishes lower bounds for private PAC learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for proving lower bounds on the sample complexity of realizable PAC learning under approximate differential privacy. This framework introduces the Independent Class Packing (ICP) parameter, which is shown to characterize private PAC sample complexity for certain classes of data. The research provides tight lower bounds for axis-aligned rectangles, unions of intervals, and linear subspaces, along with improved bounds for halfspaces and convex polygons. AI

IMPACT Establishes theoretical limits for privacy-preserving machine learning algorithms.

RANK_REASON The item is an academic paper detailing new theoretical research in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework establishes lower bounds for private PAC learning

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The item is an academic paper detailing new theoretical research in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Prathamesh Dharangutte, Amartya Sanyal ·

    New Lower Bounds for Private PAC Learning

    arXiv:2610.41980v1 Announce Type: new Abstract: We study realizable PAC learning under approximate differential privacy, and give a framework for proving lower bounds on its sample complexity. We construct classes with identical VC and Littlestone dimensions but different private…

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