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作者：Yao, Zeyu; Sun, Wenguang; Gang, Bowen
作者单位：Zhejiang University; Zhejiang University; Zhejiang University; Zhejiang University; Fudan University
摘要：Dynamic decision-making in rapidly evolving research domains, including marketing, finance, and pharmaceutical development, presents a significant challenge. Researchers frequently confront the need for real-time action within a doubly sequential framework characterized by the continuous influx of high-volume data streams and the intermittent arrival of novel tasks. This calls for the development and implementation of new online inference protocols capable of handling both the continuous proce...
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作者：Dharmakeerthi, Kulunu; Hur, YoonHaeng; Liang, Tengyuan
作者单位：University of Chicago; University of Chicago
摘要：Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shifts are often driven by unobserved confounding, and can in fact alter the concept of which model is best. This article studies distribution shifts in the domain adaptation problem with unobserved confounding. We postulate a linear structural causal model to account for endogeneity and unobserved confounding, and we le...
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作者：Meng, Xiao-Li
作者单位：Harvard University
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作者：Hingee, Kassel L.; Scealy, Janice L.; Wood, Andrew T. A.
作者单位：Australian National University
摘要：Symmetric matrices (tensors) are measured in geophysics and other disciplines, including in medical imaging, and typically their eigenvalues have valuable scientific interpretations. We design pivotal bootstrap hypothesis tests of specified eigenvalues or eigenvalue multiplicities in one-sample situations and for equal eigenvalues in k-sample situations. Our tests are more broadly applicable than existing tests by allowing very general distributions, allowing three or more samples, and account...
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作者：Xie, Zhongming; Zhang, Wanheng; Wang, Jingshen; Wu, Chong
作者单位：University of California System; University of California Berkeley; University of Texas System; UTMD Anderson Cancer Center
摘要：In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses using summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropy issues. To address ...
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作者：Song, Difan; Lewis, William E.; Knapp, Patrick F.; Wu, C. F. Jeff; Joseph, V. Roshan
作者单位：University System of Georgia; Georgia Institute of Technology; United States Department of Energy (DOE); Sandia National Laboratories
摘要：The configurations of instruments fielded on an experiment affect the amount of information captured and the quality of subsequent inference. We investigate the problem of optimizing plasma x-ray radiation detectors in a magneto-inertial fusion experiment at Sandia National Laboratories. It is impossible to directly measure properties such as the temperature of the thermonuclear fusion plasma produced in these experiments because of the extreme environment and destructive nature of the experim...
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作者：Luo, Tianpai; Wu, Weichi
作者单位：Tsinghua University
摘要：We propose a new framework for the simultaneous inference of monotone and smoothly time-varying functions under complex temporal dynamics. This will be done using the monotone rearrangement and the nonparametric estimation. We capitalize the Gaussian approximation for the nonparametric monotone estimator and construct the asymptotically correct simultaneous confidence bands (SCBs) using designed bootstrap methods. We investigate two general and practical scenarios. The first is the simultaneou...
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作者：Sohn, Jinwon; Song, Qifan
作者单位：University of Chicago; Purdue University System; Purdue University
摘要：A generative adversarial network (GAN) has been a representative backbone model in generative artificial intelligence (AI) because of its powerful performance in capturing intricate data-generating processes. However, the GAN training is well-known for its notorious training instability, usually characterized by the occurrence of mode collapse. Through the lens of gradients' variance, this work particularly analyzes the training instability and inefficiency in the presence of mode collapse by ...
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作者：Ma, Ting Fung; Kopczewska, Katarzyna
作者单位：University of South Carolina System; University of South Carolina Columbia; Universidade Comunitaria Regional de Chapeco
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作者：Wang, Hua; Gao, Sheng; Zhang, Huanyu; Shen, Milan; Su, Weijie; Wu, Jiayuan
作者单位：University of Pennsylvania
摘要：In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks. As such, an important question is how to efficiently compute the overall privacy loss under composition. This article introduces the Edgeworth Accountant, an analytical approach to composing differential privacy guarantees for private algorithms. Leveraging the f-differential privacy framework (Dong, Roth, and Su), the Edgeworth Accountant accurately tracks pr...
