SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Muhammet Sami Yavuz [ view email ] [v1] Sat, 4 Jul 2026 21:04:57 UTC (2,164 KB) Full-text links: Access Paper: View a PDF of the paper titled SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data, by Muhammet Sami Yavuz and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs q-bio 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 Connect

ed Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Uncertainty-gated selection for block

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Thomas Rossi [ view email ] [v1] Sat, 4 Jul 2026 20:41:42 UTC (28 KB) Full-text links: Access Paper: View a PDF of the paper titled Uncertainty-gated selection for block-sparse attention, by Thomas Rossi View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs 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? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite S

mart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhoujie Hou [ view email ] [v1] Sat, 4 Jul 2026 13:45:39 UTC (1,399 KB) Full-text links: Access Paper: View a PDF of the paper titled Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS--ANS Dynamic, by Zhoujie Hou and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs 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? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wentao Lu [ view email ] [v1] Sat, 4 Jul 2026 12:58:26 UTC (72 KB) Full-text links: Access Paper: View a PDF of the paper titled ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning, by Wentao Lu View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs 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? ) Litmap

s Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

LLT: Local Linear Transformer for PDE Operator Learning

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Oded Ovadia [ view email ] [v1] Sat, 4 Jul 2026 11:07:09 UTC (9,587 KB) Full-text links: Access Paper: View a PDF of the paper titled LLT: Local Linear Transformer for PDE Operator Learning, by Oded Ovadia 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 math 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? ) Litmaps Toggl

e Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

长尾胸部X光分类中的亚组漏诊问题研究

近日，一项发表于arXiv的研究聚焦于长尾分布下胸部X光分类的亚组漏诊现象。论文由Ha-Hieu Pham等学者完成，题为“Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification”。研究指出，在医学影像分类任务中，数据常呈现长尾分布，导致模型对罕见疾病或特定亚组的诊断性能显著下降，产生系统性漏诊。通过引入阈值化分析方法，工作量化了不同亚组间的诊断差异，揭示了模型在尾部类别上的公平性缺陷。该成果为提升临床AI模型的鲁棒性与公平性提供了重要参考，有助于推动医学影像辅助诊断的可靠部署。 #医学影像 #AI #长尾分布 #胸部X光 #分类 #漏诊 #公平性 #arXiv #研究

时序图网络可解释性新方法

arXiv上的一篇新论文提出了一种针对时序图网络（Temporal Graph Networks）的可解释性方法，名为“内存回溯与拓扑归因”（Memory Backtracking and Topological Attribution）。该方法通过回溯模型的内存状态并分析图拓扑结构，为时序图模型的预测结果提供可解释的归因。研究旨在解决时序图网络在动态图任务中“黑箱”特性的问题，提升模型在社交网络分析、金融交易检测等场景下的可解释性和可信度。 #时序图网络 #可解释性 #内存回溯 #拓扑归因 #机器学习 #人工智能 #论文

最大似然估计的子高斯集中与熵正态性研究

近日，一篇题为“Sub-Gaussian Concentration and Entropic Normality of the Maximum Likelihood Estimator”的学术论文在预印本平台arXiv上发布。该论文由Leighton P. Barnes与Alex Dytso共同撰写，聚焦于最大似然估计（MLE）的理论性质，证明了MLE在特定条件下具有子高斯集中行为以及熵正态性。这一成果为高维统计推断和机器学习算法的收敛性分析提供了新的数学工具。论文自2026年5月首次提交以来，已更新多个版本，受到相关领域研究者关注。 #最大似然估计 #子高斯集中 #熵正态性 #arXiv #统计学习 #信息论 #机器学习 #高维统计

Telegram必备的搜索引擎，极搜JISOU帮你精准找到，想要的群组、频道、视频、音乐

👉 t.me/jisou2?start=a_8247614025

Wasserstein 分布鲁棒遗憾优化

一篇题为《Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback》的学术论文在 arXiv 上发布。该研究聚焦于从人类反馈中强化学习（RLHF）的鲁棒性问题，提出了一种基于 Wasserstein 距离的分布鲁棒遗憾优化方法。传统 RLHF 在面对人类偏好分布偏移时容易产生不稳定的策略，这项新工作通过最小化在最坏情况分布下的遗憾界，增强了模型对偏好不确定性的抵御能力。论文作者来自相关研究机构，目前该工作已公开完整 PDF 与代码链接，供学界进一步探讨。 #人工智能 #强化学习 #RLHF #分布鲁棒优化 #Wasserstein #机器学习 #arXiv

超越固定错误发现率

该论文由Meiyi Zhu和Osvaldo Simeone共同完成，提出一种基于E变量的新方法，用于在多重假设检验中进行事后共形选择。传统方法通常需要预设一个固定的错误发现率（FDR），而本研究通过引入E变量，允许用户在分析完成后灵活调整错误控制标准，无需事先设定阈值。这一框架结合了共形推断的分布自由特性与E变量的可交换性优势，能够在保持有效性的同时提供更强的适应性。该方法在统计推断、机器学习和科学发现等领域具有广泛的应用前景，为解决高维数据中的多重比较问题提供了全新思路。 #arXiv #统计 #机器学习 #共形推断 #E变量 #假发现率 #事后分析 #学术论文 #统计学新方法

鲁棒性量化新指标

该研究由 Rodrigo F. L. Lassance 和 Jasper De Bock 提出，针对判别模型鲁棒性量化问题，定义了一种全新的鲁棒性度量指标，并将其应用于动态分类器选择（Dynamic Classifier Selection）领域。传统鲁棒性评估多依赖对抗样本或经验方法，而该工作从理论层面构建了更系统的量化框架，能够更准确地衡量模型在面对输入扰动时的稳定性。通过将新指标嵌入分类器选择流程，研究证明这一方法可显著提升集成学习系统的泛化能力和抗干扰性能。论文已提交至 arXiv，并提供了完整的数学推导与实验验证，为机器学习安全性和可靠性研究提供了重要工具。 #鲁棒性 #判别模型 #动态分类器选择 #机器学习 #arXiv #人工智能 #安全性

研究提出干预下有效选择性共形推断的部分因果结构学习方法

据arXiv预印本平台，一篇题为《Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions》的论文近日发布。该论文由Amir Asiaee等人撰写，提出了一种部分因果结构学习框架，旨在确保在干预条件下进行选择性共形推断的有效性。传统共形推断在干预环境中可能失效，而该方法通过部分因果结构学习，在无需完全识别因果图的情况下，仍能保证推断的覆盖率和选择性。研究为因果推断与不确定性量化提供了新思路，对机器学习、统计学及因果科学交叉领域具有重要价值。论文于2026年3月首次提交，7月更新第二版。 #因果推断 #共形推断 #机器学习 #统计学 #AI #学术论文 #arXiv #因果结构学习

部分模型共享实现通信高效与拜占庭鲁棒的联邦共形预测

联邦学习在分布式机器学习中广泛应用，但面临通信开销大和拜占庭攻击的威胁。共形预测作为一种不确定性量化方法，在联邦场景下也需考虑鲁棒性。最新研究提出通过部分模型共享的策略，在保证拜占庭鲁棒性的同时显著降低通信成本，为联邦共形预测提供了高效安全的解决方案。该方法有望推动联邦学习在敏感领域的实际应用。 #联邦学习 #拜占庭鲁棒 #共形预测 #通信效率 #部分模型共享 #机器学习 #AI #论文

基于大语言模型的测试时学习实现量子电路生成

近日，一篇题为《通过大语言模型测试时学习生成量子电路》的论文预印本在arXiv平台发布。该研究由Adriano Macarone-Palmieri和Rosario Lo Franco共同完成，提出了一种利用大语言模型在测试阶段进行学习以生成量子电路的新方法。传统量子电路生成依赖物理模拟或复杂优化，而该方法借助大语言模型的推理能力，在测试时动态调整，无需重新训练即可适应不同量子计算任务。实验表明，该技术能有效提升量子电路的生成效率与准确性，为量子计算与人工智能的交叉融合提供了新思路，有望加速量子算法设计与自动化。 #量子计算 #大语言模型 #AI #量子电路 #论文 #arXiv #测试时学习 #人工智能

多分布鲁棒共形预测新方法提出

近日，一篇题为《Multi-Distribution Robust Conformal Prediction》的论文在arXiv预印本平台发布。该研究由Yuqi Yang等人完成，提出了一种针对多分布场景的鲁棒共形预测框架。共形预测是一种不依赖模型假设的置信区间构建方法，而新方法通过优化多个分布下的最坏情况性能，显著提升了预测在分布偏移时的稳健性。论文于2026年1月首次提交，同年7月更新，为不确定性量化领域提供了重要理论拓展。 #共形预测 #鲁棒性 #多分布 #机器学习 #不确定性量化 #arXiv #论文