-
作者:Bordino, Alberto; Klopp, Olga
作者单位:University of Warwick; ESSEC Business School
-
作者:Stehlik, Milan; Schlather, Martin
作者单位:Universidad de Valparaiso; University of Mannheim
-
作者:Xie, Dongyue; Gui, Lin; Wang, Jingshu
作者单位:University of Chicago
摘要:Integrating heterogeneous datasets across different measurement platforms poses fundamental challenges for statistical inference. An important example is cell type deconvolution, where cell type proportions in bulk RNA-seq data are estimated using reference single-cell data from different sources, leading to platform-specific scaling effects, measurement noise, and biological heterogeneity. Existing methods often treat estimated proportions as observed in downstream analyses, potentially compr...
-
作者:Chen, Yuan; Gerber, Mathieu; Andrieu, Christophe; Douc, Randal
作者单位:University of Bristol; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom SudParis
摘要:We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter theta. A popular idea to address this problem consists of incorporating theta in the state of the system and allowing its time evolution, modelled as a Markov chain (theta t)t >= 1. This proxy model defines a so-called self-organizing SSM (SO-SSM) to which one may apply standard particle filters. However, the practical implementation of this idea in a theoreticall...
-
作者:Yan, Yuling; Su, Weijie J.; Fan, Jianqing
作者单位:University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania; Princeton University
摘要:In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their papers by perceived quality. In this paper, we leverage these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences by extending the isotonic mechanism to exponential family distributions. This mechanism produces adjusted scores closely aligned with the original scores while strictly adhering to the author-specified ran...
-
作者:Cai, T. Tony; Chakraborty, Abhinav; Vuursteen, Lasse
作者单位:University of Pennsylvania; Columbia University; Duke University
摘要:Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range of applications. This paper addresses the problem of optimal functional mean estimation from discretely sampled data in a federated setting. We consider a heterogeneous framework where the number of individuals, measurements per individual, and privacy parameters vary across one or more servers, under both common and...
-
作者:Rotnitzky, Andrea; Smucler, Ezequiel; Robins, James
作者单位:University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
摘要:We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
-
作者:Shan, Jiawei; Ying, Chao; Zhao, Jiwei
作者单位:University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
-
作者:Srakar, Andrej
作者单位:Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana
-
作者:Tang, Boxin
作者单位:Simon Fraser University
摘要:The maximin distance is an attractive criterion for constructing space-filling designs. As factors in computer experiments are generally quantitative, the L-p-distance is appropriate. Theoretical construction of maximin L-p-distance designs, however, is extremely challenging. Given that directly attacking the problem is difficult, we propose an indirect approach that first constructs maximin Hamming distance designs and then constructs maximin L-p-distance designs using the former. The approac...