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Diffusion Models in Simulation-Based Inference

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jonas Arruda [ view email ] [v1] Mon, 22 Dec 2025 15:10:35 UTC (8,821 KB) [v2] Thu, 29 Jan 2026 14:33:29 UTC (8,870 KB) [v3] Wed, 8 Jul 2026 15:20:55 UTC (9,510 KB) Full-text links: Access Paper: View a PDF of the paper titled Diffusion Models in Simulation-Based Inference: A Tutorial Review, by Jonas Arruda and Niels Bracher and Ullrich K\"othe and Jan Hasenauer and Stefan T. Radev View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2025-12 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 Bibliog

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Fast segmentation of watermarked texts from large language models through an epidemic change

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Subhrajyoty Roy [ view email ] [v1] Thu, 25 Sep 2025 13:44:34 UTC (768 KB) [v2] Wed, 8 Jul 2026 16:16:06 UTC (2,347 KB) Full-text links: Access Paper: View a PDF of the paper titled Fast segmentation of watermarked texts from large language models through an epidemic change-point framework, by Soham Bonnerjee and 1 other authors View PDF TeX Source view license Current browse context: < prev | next > new | recent | 2025-09 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 Bibliograph

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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