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New paper corrects memorization lower bound in machine learning

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv by Zhenisbek Assylbekov and Lisa Feldman Barrett corrects a previously established memorization lower bound. The authors provide a counterexample to the bound's conditional and averaged forms, demonstrating that the ratio of the proposed penalty to actual excess risk can grow exponentially. They propose an exact replacement using full-sample posterior probabilities, which depend on multiplicity and the complete frequency profile, and characterize the largest multiplicity-only coefficients valid for unrestricted learners. AI

IMPACT Refines theoretical understanding of memorization in machine learning models.

RANK_REASON The cluster contains a research paper published on arXiv detailing a correction to a theoretical concept 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 paper corrects memorization lower bound in machine learning

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The cluster contains a research paper published on arXiv detailing a correction to a theoretical concept 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) · Zhenisbek Assylbekov, Rustem Takhanov ·

    Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound

    arXiv:2610.20402v1 Announce Type: cross Abstract: Feldman's memorization lower bound assigns a frequency coefficient based on how often a point appears in the training sample. We give an exact three-point counterexample to its conditional and averaged forms, using independent fai…

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