3期

最新文章

• Jiawei Shan, Chao Ying, and Jiwei Zhao's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  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
• Seconder of the vote of thanks to Bruns-Smith et al. and contribution to the Discussion of 'Augmented balancing weights as linear regression' AI翻译
  作者：Srakar, Andrej
  作者单位：Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute; University of Ljubljana
• Multiple randomization designs: estimation and inference with interference AI翻译
  作者：Masoero, Lorenzo; Vijaykumar, Suhas; Richardson, Thomas S.; McQueen, James; Rosen, Ido; Burdick, Brian; Bajari, Pat; Imbens, Guido
  作者单位：Amazon.com; University of Washington; University of Washington Seattle; Stanford University; Stanford University
  摘要：Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for 'Simple Multiple Randomization Designs', a new class...
• Authors' reply to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Bruns-Smith, David; Dukes, Oliver; Feller, Avi; Ogburn, Elizabeth L.
  作者单位：Stanford University; Ghent University; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
• Charles R. Doss and Jared D. Huling's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Doss, Charles R.; Huling, Jared D.
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Doss and Huling's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al.
• Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints AI翻译
  作者：Auddy, Arnab; Cai, T. Tony; Chakraborty, Abhinav
  作者单位：University System of Ohio; Ohio State University; University of Pennsylvania; Columbia University
  摘要：This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing diverse sample sizes, varying privacy parameters, and data heterogeneity across different servers. We first establish the minimax misclassification rate, precisely characterizing the effects of privacy constraints, source samples, and target samples on...
• Coloured Gaussian directed acyclic graphical models AI翻译
  作者：Boege, Tobias; Kubjas, Kaie; Misra, Pratik; Solus, Liam
  作者单位：UiT The Arctic University of Tromso; Aalto University; State University of New York (SUNY) System; Binghamton University, SUNY; Royal Institute of Technology
  摘要：We study submodels of Gaussian directed acyclic graph (DAG) models defined by partial homogeneity constraints imposed on the model error variances and structural coefficients. We represent these models with coloured DAGs and investigate their properties for use in statistical and causal inference. Local and global Markov properties are provided and shown to characterize the coloured DAG model. Additional properties relevant to causal discovery are studied, including the existence and nonexiste...
• Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives AI翻译
  作者：Behdin, Kayhan; Loewinger, Gabriel; Kishida, Kenneth T.; Parmigiani, Giovanni; Mazumder, Rahul
  作者单位：Massachusetts Institute of Technology (MIT); National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH); Wake Forest University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard T.H. Chan School of Public Health; Massachusetts Institute of Technology (MIT)
  摘要：We consider a problem in multi-task learning (MTL) where multiple linear models are jointly trained on a collection of datasets ('tasks'). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and the values of non-zero coefficients to differ across tasks while still leveraging partially shared structure. Our methods encourage models to share information across tasks through separately encouraging (1) coefficient supports, and/or (2) nonzero coefficie...