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作者：Liu, Yang; Goudie, Robert J. B.
作者单位：University of Cambridge; MRC Biostatistics Unit
摘要：Standard Bayesian inference enables building models that combine information from various sources, but this inference may not be reliable if components of the model are misspecified. Cut inference, a particular type of modularized Bayesian inference, is an alternative that splits a model into modules and cuts the feedback from any suspect module. Previous studies have focused on a two module case, but a more general definition of a 'module' remains unclear. We present a formal definition of a ...
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作者：Gazin, Ulysse; Heller, Ruth; Marandon, Ariane; Roquain, Etienne
作者单位：Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite; Universite Paris Cite; Tel Aviv University; Alan Turing Institute; Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite
摘要：In supervised learning, including regression and classification, conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider here the case where such prediction sets come after a selection process. The selection process requires that the selected prediction sets be 'informative' in a well-defined sense. We consider both the classification and regression settings where the analyst may consider as informative only th...
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作者：Wang, Shulei
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications. Despite the empirical effectiveness, most existing methods lack theoretical understanding under a general nonlinear setting. To fill this gap, we develop a statistical f...
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作者：Chang, Jinyuan; Tang, Cheng Yong; Zhu, Yuanzheng
作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Southwestern University of Finance & Economics - China
摘要：In this study, we introduce a novel methodological framework called Bayesian penalized empirical likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to facilitate the use of well-established Markov Chain Monte Carlo sampling schemes as a convenient alternative to the complex optimization typi...
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作者：Salomone, Robert; South, Leah F.; Drovandi, Christopher; Kroese, Dirk P.; Johansen, Adam M.
作者单位：Queensland University of Technology (QUT); Queensland University of Technology (QUT); University of Queensland; University of Warwick
摘要：We introduce a new class of sequential Monte Carlo methods which reformulates the essence of the nested sampling (NS) method of Skilling in terms of sequential Monte Carlo techniques. Two new algorithms are proposed: nested sampling via sequential Monte Carlo (NS-SMC) and adaptive nested sampling via sequential Monte Carlo (ANS-SMC). The new framework allows convergence results to be obtained in the setting when Markov chain Monte Carlo (MCMC) is used to produce new samples. An additional bene...
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作者：Wei, Waverly; Ma, Xinwei; Wang, Jingshen
作者单位：University of Southern California; University of California System; University of California San Diego
摘要：Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analysing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the design of randomized experime...
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作者：Yiu, Andrew; Fong, Edwin; Holmes, Chris; Rousseau, Judith
作者单位：University of Oxford; University of Hong Kong; University of Oxford; Universite PSL; Universite Paris-Dauphine; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
摘要：We present a new approach to semiparametric inference using corrected posterior distributions. The method allows us to leverage the adaptivity, regularization, and predictive power of nonparametric Bayesian procedures to estimate low-dimensional functionals of interest without being restricted by the holistic Bayesian formalism. Starting from a conventional posterior on the whole data-generating distribution, we correct the marginal posterior for each functional of interest with the help of th...
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作者：Evans, Robin J.; Didelez, Vanessa
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作者：Gibbs, Isaac; Cherian, John J.; Candes, Emmanuel J.
作者单位：Stanford University; Stanford University
摘要：We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact conditional coverage universally in finite samples. Thus, most popular methods only guarantee marginal coverage over the covariates or are restricted to a limited set of conditional targets, e.g. coverage over a finite set of prespecified subgroups. This paper bridges this gap by defining a spectrum of problems that int...
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作者：Whitehouse, Michael; Whiteley, Nick; Rimella, Lorenzo
