Learning the differential statistical dependency network between two contexts is essential for many real-life applications, mostly in the high dimensional low sample regime. In this paper, we propose a novel differential network estimator that allows integrating various sources of knowledge beyond data samples. The proposed estimator is scalable to a large number of variables and achieves a sharp asymptotic convergence rate. Empirical experiments on extensive simulated data and four real-world applications (one on neuroimaging and three from functional genomics) show that our approach achieves improved differential network estimation and provides better supports to downstream tasks like classification. Our results highlight significant benefits of integrating group, spatial and anatomic knowledge during differential genetic network identification and brain connectome change discovery.
Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory（橡树岭国家实验室纳米相材料中心）；University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee（田纳西大学橡树岭创新研究所）；Department of Material Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo（东京科学大学材料科学与工程系、材料与化学技术学院）；Department of Materials Science and Engineering, Norwegian University of Science and Technology (NTNU)（挪威科学技术大学（NTNU）材料科学与工程系）；Faculty of Physics and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen（杜伊斯堡- Essen大学物理系和杜伊斯堡- Essen纳米集成中心（CENIDE））；Research Center Future Energy Materials and Systems, Research Alliance Ruhr（鲁尔研究联盟未来能源材料与系统研究中心）
Multimodal Scientific Learning Beyond Diffusions and Flows
Graduate Group in Applied Mathematics and Computational Science（应用数学与计算科学联合研究生组）；Department of Computer and Information Science（计算机与信息科学系）；Department of Mechanical Engineering and Applied Mechanics（机械工程与应用力学系）
Scientifically-Interpretable Reasoning Network (ScIReN): Discovering Hidden Relationships in the Carbon Cycle and Beyond
Department of Informatics and Intelligent Systems, Institute of Energy and the Environment, The Pennsylvania State University（信息学与智能系统系，能源与环境研究所，宾夕法尼亚州立大学）
Beyond a Better Score: Long-Horizon Agentic ML Development and Evaluation Protocol for Physics Time Series
Halıcıoğlu Data Science Institute, University of California San Diego（加州大学圣迭戈分校哈利乔格鲁数据科学学院）；Department of Physics, University of California San Diego（加州大学圣迭戈分校物理系）
SEAM: Global consistency beyond local accuracy in scientific machine learning
Axiom Research Group（阿克西姆研究集团）；The Nelson Mandela African Institution of Science and Technology（纳尔逊·曼德拉非洲科学技术研究院）；African Institute for Mathematical Sciences（非洲数学科学研究所）；The University of Tokyo（东京大学）
Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration
Quantellence Research（Quantellence研究公司）；St. Petersburg Department of the Steklov Institute of Mathematics（斯捷克洛夫数学研究所圣彼得堡分部）；St. Petersburg State University（圣彼得堡国立大学）
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子工程与信息学院，米兰理工学院）；Osservatorio Astronomico di Roma, INAF（罗马天文台，INAF）；AstroAI, Center for Astrophysics | | Harvard & Smithsonian（AstroAI，哈佛与史密松尼天体物理中心）；Center for Computational Astrophysics, Institute for Advanced Study/The Flatiron Institute（计算天文学中心，高级研究院/Flatiron研究所）
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights
Argonne National Laboratory（阿贡国家实验室）；The University of Chicago（芝加哥大学）；NVIDIA Inc.（NVIDIA公司）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；California Institute of Technology（加州理工学院）
How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review
University of Pennsylvania（宾夕法尼亚大学）；Yale University（耶鲁大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；New York University（纽约大学）；Princeton University（普林斯顿大学）
Learning-guided Kansa collocation for forward and inverse PDEs beyond linearity
Department of Computer Science and Technology（计算机科学与技术系）；Department of Applied Mathematics and Theoretical Physics（应用数学与理论物理系）；University of Cambridge（剑桥大学）
