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Optimal Conformal Prediction under Epistemic Uncertainty

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alireza Javanmardi [ view email ] [v1] Sun, 25 May 2025 08:32:44 UTC (110 KB) [v2] Wed, 8 Jul 2026 13:19:58 UTC (328 KB) Full-text links: Access Paper: View a PDF of the paper titled Optimal Conformal Prediction under Epistemic Uncertainty, by Alireza Javanmardi and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2025-05 Change to browse by: cs stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Co

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Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haotian Lin [ view email ] [v1] Sat, 18 Jan 2025 20:33:37 UTC (234 KB) [v2] Tue, 7 Jul 2026 22:00:00 UTC (156 KB) Full-text links: Access Paper: View a PDF of the paper titled Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer, by Haotian Lin and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2025-01 Change to browse by: cs stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliogra

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Avoiding unsafe sets when training with Langevin Dynamics

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Adam Oberman [ view email ] [v1] Wed, 8 Jul 2026 15:36:45 UTC (184 KB) Full-text links: Access Paper: View a PDF of the paper titled Avoiding unsafe sets when training with Langevin Dynamics, by Adam M. Oberman View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs math stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers?

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Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Adam Oberman [ view email ] [v1] Wed, 8 Jul 2026 15:11:21 UTC (76 KB) Full-text links: Access Paper: View a PDF of the paper titled Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization, by Adam M. Oberman View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs math stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Tog

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The Optimal Sample Complexity of Learning Autoregressive Chain-of

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhiyuan Li [ view email ] [v1] Wed, 8 Jul 2026 13:49:54 UTC (33 KB) Full-text links: Access Paper: View a PDF of the paper titled The Optimal Sample Complexity of Learning Autoregressive Chain-of-Thought, by Zhiyuan Li View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connect

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Gauge-Invariant Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jiaqing Xie [ view email ] [v1] Wed, 8 Jul 2026 06:05:54 UTC (57 KB) Full-text links: Access Paper: View a PDF of the paper titled Gauge-Invariant Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces, by Jiaqing Xie and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle

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Local large deviations for linear-region growth in random piecewise

Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Christian Hirsch [ view email ] [v1] Wed, 8 Jul 2026 05:14:19 UTC (150 KB) Full-text links: Access Paper: View a PDF of the paper titled Local large deviations for linear-region growth in random piecewise-linear networks, by Recep \"Ozkan and Christian Hirsch View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: math stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connecte

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迁移学习与线性判别分析

近日，一篇题为“Transfer Learning for Linear Discriminant Analysis with a Shared Classification Signal”的研究论文在arXiv平台上提交。该论文由Yonghan Zhang等三位作者完成，主要聚焦于迁移学习在共享分类信号场景下与线性判别分析（LDA）的结合。研究旨在利用源域中共享的分类信号来提升目标域的分类性能，为统计学习中的迁移应用提供了新的理论视角和方法框架。论文目前以PDF格式公开，并提供了HTML预览及TeX源码，供学界深入探讨。该工作预计将对机器学习、统计分类以及跨领域知识迁移等领域产生积极影响。 #迁移学习 #线性判别分析 #共享分类信号 #arXiv #机器学习 #统计学 #论文

生成式代理助力最佳臂识别研究

一篇题为《Best-Arm Identification with Generative Proxy》的学术论文近日在arXiv预印本平台上发布。该论文由Tianyi Ma等四位作者共同撰写，提交于2026年7月8日。研究聚焦于强化学习中的最佳臂识别问题，提出了一种利用生成式代理（Generative Proxy）来提升识别效率的新方法。通过引入生成模型作为代理，该方法有望在更少的采样次数内准确识别出最优选项，从而降低实验成本并加速决策过程。这一创新为多臂老虎机及在线学习领域提供了新的理论视角和实用工具。 #机器学习 #最佳臂识别 #生成模型 #强化学习 #arXiv #学术论文 #AI

从跳跃到签名

据arXiv预印本显示，研究人员Niels Cariou-Kotlarek和Vasileios Lampos提出了一种名为“从跳跃到签名”的生成方法，用于时间点过程建模。该工作利用签名（signatures）技术对事件序列的跳跃特征进行表征，能够有效生成符合真实动态的时间点过程数据。这一方法有望在金融、流行病学、网络分析等领域的事件序列生成与预测中发挥重要作用。论文已提交至arXiv平台，目前可供查阅。 #时间点过程 #生成模型 #签名 #机器学习 #统计建模 #arXiv #学术研究 #事件序列

虚拟筛选校准中的静默失败：边际一致性预测遗漏少数类，条件性修复恢复之

一项新研究揭示了在虚拟筛选中使用边际一致性预测时存在的隐蔽缺陷。该方法在整体校准表现看似良好时，却会系统性遗漏少数类（如活性化合物），导致假阴性率升高。研究者提出了一种基于类别条件的修复策略，通过为每个类别分别进行校准，显著恢复了模型对少数类的覆盖能力。实验表明，该修复方法在保持全局校准性的同时，有效提升了稀有类别的预测可靠性，对药物发现等应用场景具有重要实践意义。 #机器学习 #虚拟筛选 #一致性预测 #类别不平衡 #药物发现 #AI #校准 #少数类

研究人员提出统一框架检测AI生成内容与伪影

近日，一篇发表于arXiv预印本平台的论文提出了一种名为“统一检测框架”的新方法，旨在识别AI相关内容和合成伪影。该框架由张西峰等三位研究人员共同开发，能够高效检测各类AI生成文本、图像及代码中的伪造痕迹。研究团队表示，当前AI内容泛滥，现有检测工具往往局限于特定类型，而该框架实现了跨模态的统一检测，有望提升虚假信息识别能力。论文已通过DataCite分配DOI，并提供了HTML、TeX源码及多种引用格式。该工作属于计算机科学与统计学交叉领域，为AI安全治理提供了新思路。 #AI检测 #统一框架 #伪影识别 #arXiv #人工智能安全 #计算机科学 #学术研究