研究人员开发了一种用于自适应选择一致风险控制的新有限样本证明，旨在提高选择性预测器的安全性和实用性。该证明同时界定了选择的风险、接受概率和部署效用，提供了一种比以前方法更精细的途径。在ImageNet和COCO等数据集上的实证结果表明，与现有技术相比，认证接受率有了显著提高。 AI
影响 通过提供更严格的风险和接受概率界限来增强AI系统的可靠性。
排序理由 该集群包含一篇在arXiv上发表的关于新技术方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2606.08517v1 Announce Type: new Abstract: Selective predictors answer on confident inputs and abstain elsewhere; deploying one safely needs a single finite-sample certificate that simultaneously upper-bounds the selected risk, lower-bounds the acceptance probability $\pacc$…
Selective predictors answer on confident inputs and abstain elsewhere; deploying one safely needs a single finite-sample certificate that simultaneously upper-bounds the selected risk, lower-bounds the acceptance probability $\pacc$ above a floor $\pmin$, and lower-bounds the dep…
arXiv:2510.07750v3 Announce Type: replace Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficie…