ACS让数据筛选过程可交互且误差可控，支持边分析边决策。
ACS: An interactive framework for conformal selection
- 通过控制决策信息流，实现人机协同的自适应数据探索
- 在真实数据中验证了对大模型和药物发现的有效性
- 适合需要动态调整筛选策略的研究者
本文提出自适应共形选择（ACS），一种无需依赖模型的交互式选择框架，具备严格的错误率控制能力。基于共形选择（Jin and Candès, 2023b），ACS 支持人机协同的自适应数据分析：可在不违反错误率约束的前提下部分重用数据、实时决策并融入新信息或偏好。其核心在于设计了一种信息控制机制，保障分析师在探索数据时仍能严格控制假发现率（FDR）。基于该框架，我们提供了针对模型更新/选择、多样化选择以及新增标注数据整合的具体算法。通过大量数值模拟及真实场景应用（包括大语言模型部署与药物发现）验证了 ACS 的有效性。
原文摘要 · Abstract (English)
This paper presents adaptive conformal selection (ACS), an interactive framework for model-free selection with guaranteed error control. Building on conformal selection (Jin and Candès, 2023b), ACS generalizes the approach to support human-in-the-loop adaptive data analysis. Under the ACS framework, we can partially reuse the data to boost the selection power, make decisions on the fly while exploring the data, and incorporate new information or preferences as they arise. The key to ACS is a carefully designed principle that controls the information available for decision making, allowing the data analyst to explore the data adaptively while maintaining rigorous control of the false discovery rate (FDR). Based on the ACS framework, we provide concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating newly available labeled data. The effectiveness of ACS is demonstrated through extensive numerical simulations and real-data applications in large language model (LLM) deployment and drug discovery.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。