552期

最新文章

• Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity AI翻译
  作者：Huang, Xinmeng; Xu, Kan; Lee, Donghwan; Hassani, Hamed; Bastani, Hamsa; Dobriban, Edgar
  作者单位：University of Pennsylvania; Arizona State University; Arizona State University-Tempe; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：Large and complex datasets are often collected from several, possibly heterogeneous sources. Multitask learning methods improve efficiency by leveraging commonalities across datasets while accounting for possible differences among them. Here, we study multitask linear regression and contextual bandits under sparse heterogeneity, where the source/task-associated parameters are equal to a global parameter plus a sparse task-specific term. We propose a novel two-stage estimator called MOLAR that ...
• Network-Based Neighborhood Regression AI翻译
  作者：Zhen, Yaoming; Du, Jin-Hong
  作者单位：University of Toronto; Carnegie Mellon University; Carnegie Mellon University
  摘要：Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This article proposes a novel network-based neighborhood regression framework whose regression func...
• Inferences in Multinomial Dynamic Mixed Logit Models AI翻译
  作者：Oyet, Alwell; Sutradhar, Brajendra C.; Rao, R. Prabhakar
  作者单位：Memorial University Newfoundland; Sri Sathya Sai Institute of Higher Learning
  摘要：In this article we propose a general multinomial dynamic mixed logits model which explains how a multinomial/categorical response at a given time can be affected by (a) an individual's categorical fixed covariates, (b) certain category prone random effects, and (c) an individual's past multinomial responses. This model may be considered as a generalization of the (a) existing multinomial dynamic fixed models to the mixed model setup with category prone random effects; or (b) existing standard ...
• Discovering the Network Granger Causality in Large Vector Autoregressive Models AI翻译
  作者：Uematsu, Yoshimasa; Yamagata, Takashi
  作者单位：Hitotsubashi University; University of York - UK; University of Osaka
  摘要：This article proposes novel inferential procedures for discovering the network Granger causality in high-dimensional vector autoregressive models. In particular, we mainly offer two multiple testing procedures designed to control the false discovery rate (FDR). The first procedure is based on the limiting normal distribution of the t-statistics with the debiased lasso estimator. The second procedure is its bootstrap version. We also provide a robustification of the first procedure against any ...
• Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS AI翻译
  作者：Yu, Myeonghun; Wang, Yue; Xie, Siyu; Tan, Kean Ming; Zhou, Wen-Xin
  作者单位：University of Michigan System; University of Michigan; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Northwestern University; University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：Expected shortfall (ES) has emerged as an important metric for characterizing the tail behavior of a random outcome, specifically associated with rarer events that entail severe consequences. In climate science, the threats of flooding and heatwaves loom large, impacting natural environments and human communities. In actuarial studies, a key observation in modeling insurance claim sizes is that features exhibit distinct effects in explaining small and large claims. This article concerns nonpar...
• Integrative Analysis of Microbial 16S Gene and Shotgun Metagenomic Sequencing Data Improves Statistical Efficiency in Testing Differential Abundance AI翻译
  作者：Yue, Ye; Mao, Yicong; Read, Timothy D.; Fedirko, Veronika; Satten, Glen A.; Chen, Xuan; Zhan, Xiang; Hu, Yi-Juan
  作者单位：Emory University; Rollins School Public Health; Peking University; Emory University; University of Texas System; UTMD Anderson Cancer Center; Emory University; Rollins School Public Health; Emory University; Huazhong Agricultural University; Southeast University - China; Peking University; Peking University
  摘要：The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Surprisingly, many microbiome studies have performed both experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, suggesting that an integrative analysis of both datasets could enhance the testing power for these signatures. However, differential experimental biases, partially overlappin...
• Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes AI翻译
  作者：Du, Jin-Hong; Zeng, Zhenghao; Kennedy, Edward H.; Wasserman, Larry; Roeder, Kathryn
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
  摘要：With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels of interest are not directly observable; instead, only repeated proxy measurements from each individual's cells are available, providing a derived outcome to estimate the underlying outcome for each of many genes. In this article, we propose a gen...
• Asymptotic Behavior of Adversarial Training Estimator under ℓ∞-Perturbation AI翻译
  作者：Xie, Yiling; Huo, Xiaoming
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Adversarial training has been proposed to protect machine learning models against adversarial attacks. This article focuses on adversarial training under l(infinity)-perturbation, which has recently attracted much research attention. The asymptotic behavior of the adversarial training estimator is investigated in the generalized linear model. The results imply that the asymptotic distribution of the adversarial training estimator under l(infinity)-perturbation could put a positive probability ...
• Hub Detection in Gaussian Graphical Models AI翻译
  作者：Gomez, Jose A. Sanchez; Mo, Weibin; Zhao, Junlong; Liu, Yufeng
  作者单位：University of California System; University of California Riverside; Purdue University System; Purdue University; Beijing Normal University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection consists of estima...
• On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization AI翻译
  作者：Xiao, Jiancong; Li, Ziniu; Xie, Xingyu; Getzen, Emily; Fang, Cong; Long, Qi; Su, Weijie J.
  作者单位：University of Pennsylvania; Pennsylvania Medicine; The Chinese University of Hong Kong, Shenzhen; National University of Singapore; Peking University; University of Pennsylvania
  摘要：Accurately aligning large language models (LLMs) with human preferences is crucial for informing fair, economically sound, and statistically efficient decision-making processes. However, we argue that the predominant approach for aligning LLMs with human preferences through a reward model-reinforcement learning from human feedback (RLHF)-suffers from an inherent algorithmic bias due to its Kullback-Leibler-based regularization in optimization. In extreme cases, this bias could lead to a phenom...