English(EN) Delay Complexity Dimension: Learning with Delayed and Censored Labels

新指标量化延迟和审查标签的样本复杂度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员引入了延迟复杂度维度（DCD）来量化在具有延迟和审查标签的情况下进行PAC学习所需的样本数量。DCD整合了VC维度和观测通道的标签信息率，为样本复杂度设定了一个下界。一个Oracle伪标签ERM算法实现了这一速率，在合成数据和Criteo转化日志上的实验支持了理论预测。 AI

影响为理解在特定标签条件下机器学习的数据需求引入了新的理论框架。

排序理由介绍机器学习新理论概念的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 15 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 介绍机器学习新理论概念的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.LG TIER_1 English(EN) · Madhava Gaikwad · 2026-10-15 04:00

延迟复杂度维度：带延迟和审查标签的学习

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…