Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Momin Abbas [ view email ] [v1] Tue, 7 Oct 2025 22:43:57 UTC (931 KB) [v2] Mon, 6 Jul 2026 19:59:23 UTC (2,575 KB) Full-text links: Access Paper: View a PDF of the paper titled Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statistics, by Momin Abbas and Ali Falahati and Hossein Goli and Mohammad Mohammadi Amiri View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2025-10 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 Tool

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Variational Learning of Disentangled Representations

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ozgur Beker [ view email ] [v1] Fri, 20 Jun 2025 17:36:12 UTC (46,569 KB) [v2] Fri, 12 Dec 2025 20:31:20 UTC (13,444 KB) [v3] Mon, 6 Jul 2026 22:43:33 UTC (14,115 KB) Full-text links: Access Paper: View a PDF of the paper titled Variational Learning of Disentangled Representations, by Yuli Slavutsky and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2025-06 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 Expl

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选择性状态空间模型非连续门控的规则性与稳定性研究获新进展

一篇由Nikola Zubić等人提交的论文《Regularity and Stability Properties of Selective SSMs with Discontinuous Gating》已被机器学习领域权威期刊Transactions on Machine Learning Research（TMLR）接收。该论文针对具有非连续门控机制的选择性状态空间模型（SSM）展开理论分析，深入探讨了其规则性和稳定性属性。研究指出，这类模型在处理序列数据时展现出独特的动态行为，通过严格的数学推导，作者证明了在特定条件下模型能够保持稳定并表现出良好的规则性。该工作为深度学习中的高效序列建模提供了重要的理论基础，对后续优化与应用具有参考价值。论文自2025年起在arXiv上发布，历经多次修订，最终版本于2026年7月上线。 #机器学习 #状态空间模型 #TMLR #学术论文 #深度学习 #门控机制 #稳定性分析 #规则性 #AI研究

Benign Overfitting with Quantum Kernels

Focus to learn more arXiv-issued DOI via DataCite Journal reference: UAI 2026 Submission history From: Hachem Kadri [ view email ] [v1] Fri, 21 Mar 2025 10:30:42 UTC (991 KB) [v2] Tue, 7 Jul 2026 08:55:18 UTC (1,232 KB) Full-text links: Access Paper: View a PDF of the paper titled Benign Overfitting with Quantum Kernels, by Joachim Tomasi and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2025-03 Change to browse by: cs stat References & Citations INSPIRE HEP 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 To

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高维逻辑回归的普适性研究及新型CGMT在数据增强中的应用

一篇发表于2025年学习理论会议（COLT）的论文深入探讨了高维逻辑回归的普适性，并提出了一种在数据依赖条件下适用的新型凸高斯极小化定理（CGMT）。该研究由Matthew Esmaili Mallory等人完成，理论框架严格证明了高维逻辑回归的统计行为在一定条件下具有普适性，即其预测性能与噪声分布的具体形式无关。同时，研究者拓展了经典CGMT工具，使其适用于协变量存在相关性的更实际场景，并从理论上验证了数据增强技术在提升模型泛化能力方面的有效性。这一工作为高维统计学习提供了新的分析工具，对机器学习理论及实际应用具有重要意义。 #高维逻辑回归 #普适性 #CGMT #数据增强 #学习理论 #COLT2025 #机器学习 #统计学习 #理论计算机科学

二元线性分类中良性过拟合的普遍性获理论证实

近期，针对高维线性分类模型“过拟合但泛化良好”的现象，arXiv上一项新研究从理论层面揭示了其普遍性。论文题为“二元线性分类中良性过拟合的普遍性”，由Ichiro Hashimoto等学者完成。研究证明，在高维设定下，线性分类器在训练误差为零时依然能对未知数据保持优异表现，这种良性过拟合并非依赖特定模型或数据分布的特例，而是线性分类方法的本质特征。研究通过严格的数学框架，统一并拓展了此前针对特定方法的结论，对理解现代机器学习中记忆与泛化的关系提供了新视角。该论文自2025年初首次提交以来已多次更新，目前仍为预印本状态。 #机器学习 #良性过拟合 #线性分类 #高维统计 #理论证明 #人工智能 #arXiv

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研究提出部分训练三层神经网络的函数空间平均场理论

据arXiv预印本及《Journal of Machine Learning Research》2026年刊发，Zhengdao Chen等人发表论文，提出一种函数空间平均场理论，专门用于分析部分训练的三层神经网络。该理论从函数空间视角出发，通过平均场近似研究网络部分参数固定、部分训练时的动态行为，为理解浅层神经网络的训练机理与泛化特性提供了新的数学框架。相关工作已正式发表于JMLR第27卷第52期，标志着该领域理论探索的重要进展。 #机器学习 #神经网络 #平均场理论 #JMLR #深度学习 #AI #理论 #三层网络 #函数空间 #论文

Model-based Bootstrap of Controlled Markov Chains

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ziwei Su [ view email ] [v1] Tue, 12 May 2026 17:05:59 UTC (236 KB) [v2] Tue, 7 Jul 2026 05:22:23 UTC (231 KB) Full-text links: Access Paper: View a PDF of the paper titled Model-based Bootstrap of Controlled Markov Chains, by Ziwei Su and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-05 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 Conn

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基于扩散模型先验的反问题仪器与噪声参数估计研究

一篇题为《Estimation of instrument and noise parameters for inverse problem based on prior diffusion model》的论文近日在arXiv预印本平台上发布。该研究由学者Jean-François Giovannelli完成，提出了一种在反问题框架中利用扩散模型作为先验信息，同时估计仪器参数与噪声参数的方法。反问题广泛存在于图像处理、信号重建和医学成像等领域，仪器与噪声参数的不确定性常严重影响求解精度。该方法通过引入扩散模型先验，能够更准确地刻画出目标信号的分布特征，从而提升参数估计的稳定性和准确性。论文已更新至第二版本，相关代码与数据可通过平台链接获取。 #arXiv #扩散模型 #反问题 #参数估计 #图像处理 #机器学习 #学术论文

基于扩散模型的吉布斯后验采样器创新逆问题求解方法

近期在arXiv上预印的一篇论文由Jean-François Giovannelli提出了一种基于先验扩散模型的吉布斯后验采样器，用于高效解决逆问题。逆问题在医学成像、遥感等领域广泛存在，传统方法受限于先验选择的灵活性。该方法将扩散模型作为强大的先验分布，通过吉布斯采样从后验分布中迭代采样，显著提升了图像重建等任务的精度与计算效率。实验在多个逆问题基准上验证了其优越性，为贝叶斯推断与逆问题求解提供了新工具，有望推动相关应用领域的发展。 #逆问题 #扩散模型 #吉布斯采样 #贝叶斯推断 #arXiv #研究 #AI

无遗憾高斯过程优化时变函数论文在arXiv发表

一篇题为《No-Regret Gaussian Process Optimization of Time-Varying Functions》的学术论文在arXiv平台公开。该研究由Eliabelle Mauduit等人合作完成，针对现实场景中常见的时变函数优化难题，提出了一种基于高斯过程的无遗憾优化框架。该方法能够在函数随时间动态变化的情况下，通过在线学习机制持续更新代理模型，并理论上保证了累积遗憾的上界。论文经过多次修订，最终版本提供了完整的理论与算法细节，为动态环境下的贝叶斯优化、自适应控制及在线决策等应用领域提供了新的分析工具与算法支持。 #高斯过程 #无遗憾优化 #时变函数 #机器学习 #arXiv #优化算法 #动态系统

EntroPath

据arXiv预印本平台显示，研究者Przemysław Rola于2026年7月提交了题为“EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning”的论文。该工作提出一种基于最大熵原理的路径集成嵌入方法，旨在提升流形学习在高维数据降维与结构发现中的表现。通过构建路径集合并最大化其熵，算法有望更鲁棒地捕捉数据的低维流形结构，为复杂数据的可视化和特征提取提供新思路。论文目前以PDF和HTML格式公开，并已纳入arXiv的计算机科学、定量生物学与统计学等交叉领域浏览。 #流形学习 #最大熵 #EntroPath #数据降维 #机器学习 #arXiv #学术论文

深度Ritz方法实现高维定态薛定谔方程特征学习

arXiv上发表的论文提出了一种基于深度Ritz方法的特征学习框架，用于求解高维定态薛定谔方程。传统数值方法在高维问题中面临维度灾难，而该框架利用神经网络自动学习问题的关键特征，有效提升了求解效率与精度。实验表明，该方法在处理多个高维基准问题时表现出色，计算速度与准确性均优于现有技术。该成果为量子物理领域的复杂问题提供了新的数值工具，也展示了深度学习在科学计算中的巨大潜力。 #深度Ritz方法 #特征学习 #薛定谔方程 #高维方程 #科学计算 #深度学习 #数学物理 #arXiv

因子增强机器学习面板回归研究在arXiv发布

据arXiv预印本平台显示，一篇题为《因子增强机器学习面板回归》（Factor-Augmented Machine Learning Panel Regressions）的论文于2026年7月7日提交。该研究由Andrii Babii、Luca Barbaglia、Eric Ghysels和Jonas Striaukas共同完成。论文提出了一种将因子模型与机器学习方法相结合的面板回归框架，旨在更有效地处理高维面板数据中的因子结构，从而提升预测的准确性与可解释性。该工作融合了传统计量经济学与前沿机器学习工具，为宏观经济预测、金融数据建模等领域提供了新的方法论支持，展现了跨学科研究的潜力。 #因子增强 #机器学习 #面板回归 #arXiv #学术论文 #计量经济学 #高维数据 #金融科技 #新方法

正态均值估计中收缩阈值规则的近似风险最小化研究

近日，arXiv预印本收录了一篇题为“Approximate Risk Minimization Over Shrinking-Thresholding Rules in Normal Mean Estimation”的论文，作者为Wei Jiang。该研究聚焦于正态均值估计这一统计学基础问题，探讨在收缩阈值规则（如软阈值、硬阈值等）下如何实现近似风险最小化。收缩阈值方法在高维数据分析和信号处理中应用广泛，论文可能提出了新的理论框架或风险准则，以优化此类规则的选择和性能。预印本于2026年7月7日提交，为相关领域提供了新的理论视角。 #论文 #arXiv #统计学 #收缩阈值 #正态均值估计 #风险最小化 #高维统计 #预印本

Mengwu Guo 提出微分方程高斯过程逼近的统一视角

研究人员 Mengwu Guo 于2026年7月7日在arXiv预印本平台提交了一篇题为“A unified perspective of Gaussian process approximation for differential equations”的论文。该论文旨在为高斯过程在微分方程求解中的应用提供统一的理论视角，系统梳理并连接了现有多种逼近方法。高斯过程作为一种非参数贝叶斯工具，在科学计算与机器学习领域日益受到关注，而这篇论文提出的统一框架有助于加深对不同方法的理解，并可能推动新的算法设计。目前论文已发布PDF全文和HTML预览，供研究者自由获取。 #高斯过程 #微分方程 #统一视角 #arXiv #科学计算 #机器学习 #数值方法 #论文 #计算数学