2026

最新文章

• A Statistician's Overview of Physics-Informed Neural Networks for Spatio-Temporal Data AI翻译
  作者：Wikle, Christopher K.; North, Joshua; Gopalan, Giri; Yoo, Myungsoo
  作者单位：University of Missouri System; University of Missouri Columbia; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; United States Department of Energy (DOE); Los Alamos National Laboratory; University of Texas System; University of Texas Austin
  摘要：The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that...
• Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback AI翻译
  作者：Lee, Seong Jin; Sun, Will Wei; Liu, Yufeng
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Purdue University System; Purdue University; University of Michigan System; University of Michigan
  摘要：Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback, driven by diverse individual contexts and preferences, poses significant challenges for reward learning. To address this, we propose a Low-rank Contextual RLHF (LoCo-RLHF) framework that integrates contextual information to better model heterogeneous feedback while maintaining computational efficiency. Our approach b...
• Discussion of The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review AI翻译
  作者：Wang, Ying; Ren, Mengye
  作者单位：New York University
• SurvSTAAR: A Powerful Statistical Framework for Rare Variant Analysis of Time-to-Event Traits in Large-Scale Whole-Genome Sequencing Studies AI翻译
  作者：Cui, Yidan; Ma, Shiyang; Yuan, Yuxin; Zhu, Nengjie; Chen, Haifeng; Wei, Ting; Li, Zilin; Li, Xihao; Yu, Zhangsheng
  作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Northeast Normal University - China; Northeast Normal University - China; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：The increasing availability of large-scale, population-based whole-genome sequencing (WGS) data enables comprehensive analyses of rare genetic variants, which are crucial for unraveling the genetic mechanisms underlying complex traits and diseases. Time-to-event traits offer the advantage of capturing both diagnosis status and timing, facilitating the identification of genetic variants associated with age of onset, disease progression, and lifespan. However, existing methods primarily focus on...
• Adversarial Estimation of Riesz Representers AI翻译
  作者：Chernozhukov, Victor; Newey, Whitney K.; Singh, Rahul; Syrgkanis, Vasilis
  作者单位：Massachusetts Institute of Technology (MIT); Harvard University; Harvard University; Stanford University
  摘要：Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as l...
• Causal Inference in Pharmaceutical Statistics AI翻译
  作者：Buchanan, Ashley L.
  作者单位：University of Rhode Island
• Elastic Shape Analysis of Movement Data AI翻译
  作者：Borgert, J. E.; Hannig, Jan; Tucker, J. Derek; Arbeeva, Liubov; Buck, Ashley N.; Golightly, Yvonne M.; Messier, Stephen P.; Nelson, Amanda E.; Marron, J. S.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; United States Department of Energy (DOE); Sandia National Laboratories; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of Nebraska System; University of Nebraska Medical Center; University of North Carolina; University of North Carolina Chapel Hill; Wake Forest University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Osteoarthritis (OA) is a highly prevalent degenerative joint disease, and the knee is the most commonly affected joint. Biomechanical factors, particularly forces exerted during walking, are often measured in modern studies of knee joint injury and OA, and understanding the relationship among biomechanics, clinical profiles, and OA has high clinical relevance. Biomechanical forces are typically represented as curves over time, but a standard practice in biomechanics research is to summarize th...
• A Minimax Two-Sample Test for Functional Data via Grothendieck's Divergence AI翻译
  作者：Chen, Yan; Lin, Hongmei; Wang, Xueqin; Wen, Canhong
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Shanghai University of International Business & Economics; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：Our proposed approach addresses the challenges associated with nonparametric two-sample testing for densely measured functional data. These challenges stem from the high dimensionality of data and the nature of the observation scheme. We introduce a novel metric concept for random functions known as Grothendieck's divergence to overcome these challenges, which satisfies the homogeneity-zero equivalence property. Our approach uses a pre-smoothing technique on densely measured functional data an...
• SOFARI: High-Dimensional Manifold-Based Inference AI翻译
  作者：Zheng, Zemin; Zhou, Xin; Fan, Yingying; Lv, Jinchi
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Southern California
  摘要：Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the sparse singular value decomposition (SVD) within the coefficient matrix, was introduced for interpretable multi-task learning, enabling the discovery of meaningful latent feature-response association networks across different layers. However, conducting precise inference on the latent factor matrices has remained challen...
• Additive Multi-Index Gaussian Process Modeling, with Application to Multi-Physics Surrogate Modeling of the Quark-Gluon Plasma AI翻译
  作者：Li, Kevin; Mak, Simon; Paquet, J. -F; Bass, Steffen A.
  作者单位：Duke University; Vanderbilt University; Vanderbilt University; Duke University
  摘要：The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang. A critical challenge in studying the QGP is that, to reconcile experimental observables with theoretical parameters, one requires many simulation runs of a complex physics model over a high-dimensional parameter space. Each run is computationally expensive, requiring thousands of CPU hours, thus limiting physicists to only several hundred runs. Given limited train...