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New metric quantifies sample complexity for delayed and censored labels

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the Delay Complexity Dimension (DCD) to quantify the number of samples required for PAC learning with delayed and censored labels. The DCD integrates the VC dimension with the label-information rate of the observation channel, establishing a lower bound for sample complexity. An oracle pseudo-label ERM algorithm achieves this rate, with experiments on synthetic data and Criteo conversion logs supporting the theoretical predictions. AI

IMPACT Introduces a new theoretical framework for understanding data requirements in machine learning under specific label conditions.

RANK_REASON Academic paper introducing a new 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 metric quantifies sample complexity for delayed and censored labels

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Academic paper introducing a new 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) · Madhava Gaikwad ·

    Delay Complexity Dimension: Learning with Delayed and Censored Labels

    arXiv:2610.18935v1 Announce Type: cross Abstract: We study PAC learning of binary classifiers when labels arrive after random delays and are censored beyond an observation window $W$. How many samples does this observation model require? We propose a distribution-dependent answer…

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