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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...
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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 ...
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作者：Dewaskar, Miheer; Tosh, Christopher; Knoblauch, Jeremias; Dunson, David B.
作者单位：University of New Mexico; Memorial Sloan Kettering Cancer Center; University of London; University College London; Duke University
摘要：Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, there is inevitably some amount of model misspecification: outliers, data contamination or inappropriate parametric assumptions such as Gaussianity mean that most models are at best rough approximations of reality. A significant practical concern is that for certain inferences, even small amounts of model misspecificat...
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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...
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作者：Graziani, Carlo
作者单位：United States Department of Energy (DOE); Argonne National Laboratory
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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...
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作者：Zheng, Qi
作者单位：University of Louisville
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作者：Yu, Xianshi; Zhu, Ji
作者单位：University of Michigan System; University of Michigan
摘要：While relations among individuals make an important part of data with scientific and business interests, existing statistical modeling of relational data has mainly been focusing on dyadic relations, that is, those between two individuals. This article addresses the less studied, though commonly encountered, polyadic relations that can involve more than two individuals. In particular, we propose a new latent space model for hypergraphs using determinantal point processes, which is driven by th...
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作者：Liu, Dungang; Lin, Zewei; Zhang, Heping
作者单位：University System of Ohio; University of Cincinnati; Texas State University System; Texas State University San Marcos; Yale University; Yale University; Yale University
摘要：Model diagnostics is an indispensable component in regression analysis, yet it has not been well addressed in generalized linear models (GLMs). When outcome data are discrete, classical Pearson and deviance residuals have limited utility in generating diagnostic insights. This article establishes a novel diagnostic framework for GLMs and their extensions. Unlike the convention of using a point statistic as a residual, we propose to use a function as a vehicle to retain residual information. In...
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作者：Chong, Carsten H.; Todorov, Viktor
作者单位：Hong Kong University of Science & Technology; Northwestern University
摘要：We develop a nonparametric test for deciding whether volatility of an asset follows a standard semimartingale process, with paths of finite quadratic variation, or a rough process with paths of infinite quadratic variation. The test uses the fact that volatility is rough if and only if volatility increments are negatively autocorrelated at high frequencies. It is based on the sample autocovariance of increments of spot volatility estimates computed from high-frequency asset return data. By sho...
