arXivDaily arXiv每日学术速递 周一至周五更新

高校专区

Columbia University(哥伦比亚大学)

2026-07-07 至 2026-07-07 共收录 9
2605.27991 2026-07-07 stat.ML cs.LG 版本更新

Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning

深度神经网络训练作为随机效应:优化-推断对偶性

Minhao Yao, Ruoyu Wang, Xihong Lin, Lin Liu, Zhonghua Liu

机构 * Centre for Biomedical Data Science, Duke-NUS Medical School, National University of Singapore(生物医学数据科学中心,国立新加坡大学杜克-新加坡医学学校) Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA(生物统计学系,哈佛T.H. Chan公共卫生学院,马萨诸塞州波士顿,美国) Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center of Biostatistics and Data Science, Shanghai Jiao Tong University(自然科学院,MOE-LSC,数学科学学院,CMA-上海,SJTU-耶鲁联合生物统计学与数据科学中心,上海交通大学) Department of Biostatistics, Columbia University, New York, NY, USA(生物统计学系,哥伦比亚大学,纽约州纽约市,美国)

AI总结 本文提出深度神经网络训练与经典随机效应模型等价,揭示了优化-推断对偶性,并利用限制最大似然估计实现基于似然的早停规则。

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2605.20494 2026-07-07 cs.LG physics.ao-ph stat.AP 版本更新

A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

具有风依赖性路径转换的10,000年全球随机热带气旋目录(WHITS)

Jennifer Nakamura, Upmanu Lall

机构 * Lamont-Doherty Earth Observatory, Columbia University(哥伦比亚大学拉蒙特-多赫蒂地球观测站) School of Complex Adaptive Systems, Arizona State University(亚利桑那州立大学复杂适应系统学院) Earth and Environmental Engineering, Columbia University(哥伦比亚大学地球与环境工程系)

AI总结 本文提出WHITS方法,通过非参数半马尔可夫路径生成器生成全球10,000年合成气旋目录,以提高保险损失评估的可靠性。

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2512.13956 2026-07-07 cs.MA cs.AI 版本更新

AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression

AOI:通过动态调度和分层内存压缩实现的上下文感知多智能体操作

Zishan Bai, Hanxuan Chen, Jiayi Gu, Wenqian Weng, Enze Ge, Jiacheng Shi, Yichao Zhang, Zhimo Han, Riyang Bao, Xinyuan Song, Jacqueline Pang, Junfeng Hao

机构 * Columbia University(哥伦比亚大学) Hunan University(湖南大学) Central University of Finance and Economics(中央财经大学) Chongqing University(重庆大学) Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) AI Agent Lab, Vokram Group(Vokram集团人工智能代理实验室) College of William and Mary(威廉与玛丽学院) University of Texas(得克萨斯大学) Zhengzhou University of Light Industry(郑州轻工业大学) Emory University(埃默里大学) Department of Nephrology, Affiliated Hospital of Guangdong Medical University(广东医科大学附属医院肾病科)

AI总结 本文提出AOI框架,通过动态任务调度和分层内存压缩,提升复杂IT基础设施的自主运维能力,实现72.4%的上下文压缩和94.2%的任务成功率。

Comments new revision

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2504.09662 2026-07-07 cs.MA cs.AI cs.HC 版本更新

AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations

AgentDynEx: 调节多智能体模拟的机制与动态

Jenny Ma, Riya Sahni, Karthik Sreedhar, Lydia B. Chilton

机构 * Columbia University(哥伦比亚大学)

AI总结 AgentDynEx通过配置矩阵和nudging技术帮助设置多智能体模拟,平衡机制与动态,提升模拟复杂性与动态表现。

Comments 40 pages, 9 figures

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2506.22675 2026-07-07 stat.ML cs.LG 版本更新

Bayesian Invariance Modeling of Multi-Environment Data

多环境数据的贝叶斯不变性建模

Luhuan Wu, Mingzhang Yin, Yixin Wang, John P. Cunningham, David M. Blei

机构 * Department of Applied Mathematics and Statistics, Johns Hopkins University(应用数学与统计学系,约翰霍普金斯大学) Department of Statistics, Columbia University(统计学系,哥伦比亚大学) Department of Computer Science, Columbia University(计算机科学系,哥伦比亚大学) Warrington College of Business, University of Florida(佛罗里达大学沃林顿商学院) Department of Statistics, University of Michigan(统计学系,密歇根大学)

AI总结 研究多环境数据的不变性预测问题,提出贝叶斯不变性预测模型BIP,通过将不变特征索引编码为潜在变量并后验推断恢复,证明后验一致性等,设计变分近似VI-BIP,在模拟和真实数据中表现更优。

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2504.20412 2026-07-07 cs.SE cs.AI cs.OS 版本更新

kAgent: An execution-guided crash resolution agent for the Linux kernel

kAgent:一种用于Linux内核的执行引导式崩溃修复代理

Alex Mathai, Chenxi Huang, Suwei Ma, Jihwan Kim, Hailie Mitchell, Aleksandr Nogikh, Petros Maniatis, Franjo Ivančić, Junfeng Yang, Baishakhi Ray

机构 * Department of Computer Science, Columbia University(哥伦比亚大学计算机科学系) Google Inc.(谷歌公司) Google DeepMind(谷歌DeepMind)

AI总结 研究针对Linux内核崩溃修复难题,以内核开发者修复方式为灵感构建kAgent及支持工具栈,通过检查日志等步骤修复崩溃,消融特性定量分析,评估显示其能有效修复多种崩溃。

Comments Accepted to ICML, 2026; in the Deep Learning for Code Workshop. This paper was previously circulated as "CrashFixer"

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2405.19521 2026-07-07 cs.LG stat.ML 版本更新

Hierarchical Bayesian Crowdsourcing with Item Difficulty

具有项目难度的分层贝叶斯众包

Seong Woo Han, Ozan Adıgüzel, Bob Carpenter

机构 * University of Pennsylvania(宾夕法尼亚大学) Columbia University(哥伦比亚大学) Center for Computational Mathematics, Flatiron Institute(Flatiron研究所计算数学中心)

AI总结 研究针对训练用黄金标准有偏差且带噪声的问题,引入通用测量误差模型,通过添加项目难度等效应推断共识类别,还展示约束模型双峰后验的方法,并验证其拟合优度与预测准确性。

Journal ref ProbML 2026 Workshop Track

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2407.09632 2026-07-07 stat.ML cs.LG math.ST stat.ME stat.TH 版本更新

Granger Causality in Extremes

极端情况下的格兰杰因果关系

Juraj Bodik, Olivier C. Pasche

机构 * Faculty of Business and Economics, University of Lausanne(洛桑大学商学院) Department of Statistics, UC Berkeley(伯克利大学统计学系) Research Institute for Statistics and Information Science, University of Geneva(日内瓦大学统计与信息科学研究所) Department of Industrial Engineering and Operations Research, Columbia University(哥伦比亚大学工业工程与运筹学系)

AI总结 介绍极端情况下格兰杰因果关系框架,利用因果尾系数从极端事件推断因果,建立与其他因果概念等价关系,证明关键性质,提出无模型新推断方法,性能和速度优于现有方法。

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2312.15320 2026-07-07 q-bio.QM cs.CV cs.LG cs.MM q-bio.GN 版本更新

GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Text

GestaltMML:通过结合面部图像和临床文本的多模态机器学习增强罕见遗传病诊断

Da Wu, Zhanliang Wang, Hongzhuo Chen, Jingye Yang, Cong Liu, Tzung-Chien Hsieh, Elaine Marchi, Justin Blair, Peter Krawitz, Chunhua Weng, Wendy Chung, Gholson J. Lyon, Ian D. Krantz, Jennifer M. Kalish, Kai Wang

机构 * Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia(雷蒙德·G·佩尔曼细胞与分子治疗中心,费城儿童医院) Department of Mathematics, University of Pennsylvania(数学系,宾夕法尼亚大学) Department of Biomedical Informatics, Columbia University Irving Medical Center(生物医学信息学系,哥伦比亚大学伊万斯医疗中心) Department of Human Genetics, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA(人类遗传学系,纽约州发育障碍基础研究机构,纽约州史泰登岛) Division of Human Genetics, Children’s Hospital of Philadelphia(人类遗传学部,费城儿童医院) Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School(儿科系,波士顿儿童医院,哈佛医学院) Biology PhD Program, The Graduate Center, The City University of New York(生物学博士项目,纽约市立大学研究生中心) Department of Genetics, Perelman School of Medicine, University of Pennsylvania(遗传学系,宾夕法尼亚大学佩尔曼医学学院) Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania(儿科系,宾夕法尼亚大学佩尔曼医学学院) Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania(病理学与实验室医学系,宾夕法尼亚大学佩尔曼医学学院)

AI总结 研究针对罕见遗传病诊断难题,提出基于Transformer架构的多模态机器学习方法GestaltMML,整合面部图像、人口统计学信息和临床笔记,提升预测准确性,缩小诊断差距。

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