2026

最新文章

• Additive-Effect Assisted Learning AI翻译
  作者：Zhang, Jiawei; Yang, Yuhong; Ding, Jie
  作者单位：University of Kentucky; University of Minnesota System; University of Minnesota Twin Cities
  摘要：It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modelling performance. We consider a scenario where different learners hold datasets with potentially distinct variables, and their observations can be aligned by a nonprivate identifier. Their collaboration faces the following difficulties: first, learners may need to keep data values or even variable names undisclosed due to, e.g. commercial interest o...
• Pretraining and the lasso AI翻译
  作者：Craig, Erin; Pilanci, Mert; Le Menestrel, Thomas; Narasimhan, Balasubramanian; Rivas, Manuel A.; Gullaksen, Stein-Erik; Dehghannasiri, Roozbeh; Salzman, Julia; Taylor, Jonathan; Tibshirani, Robert
  作者单位：Stanford University; Stanford University; Stanford University; Stanford University; University of Bergen; Haukeland University Hospital; University of Bergen; Stanford University; Stanford Medicine
  摘要：Pre-training is a powerful paradigm in machine learning to pass information across models. For example, suppose one has a modest-sized dataset of images of cats and dogs and plans to fit a deep neural network to classify them. With pre-training, we start with a neural network trained on a large corpus of images of not just cats and dogs but hundreds of classes. We fix all network weights except the top layer(s) and fine tune on our dataset. This often results in dramatically better performance...
• Identification and multiply robust estimation in causal mediation analysis across principal strata AI翻译
  作者：Cheng, Chao; Li, Fan
  作者单位：Yale University; Yale University
  摘要：We consider assessing causal mediation in the presence of a posttreatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the posttreatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply robust estimators are consisten...
• Zhu Shen and Jose R. Zubizarreta's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Shen, Zhu; Zubizarreta, Jose R.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
• Professor Garib Nath Singh's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Singh, Garib Nath
  作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad
• Proposer of the vote of thanks to Whiteley et al. and contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' AI翻译
  作者：Wang, Tengyao
  作者单位：University of London; London School Economics & Political Science
• Bayesian analysis of product feature allocation models AI翻译
  作者：Ghilotti, Lorenzo; Camerlenghi, Federico; Rigon, Tommaso
  作者单位：University of Milano-Bicocca
  摘要：Feature allocation models are an extension of Bayesian nonparametric clustering models, where individuals can share multiple features. We study a broad class of models whose probability distribution has a product form, which includes the popular Indian buffet process. This class plays a prominent role among existing priors, and it shares structural characteristics with Gibbs-type priors in the species sampling framework. We develop a general theory for the entire class, obtaining closed form e...
• Doubly robust conditional independence testing with generative neural networks AI翻译
  作者：Zhang, Yi; Huang, Linjun; Yang, Yun; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University System of Maryland; University of Maryland College Park; Washington University (WUSTL); Washington University (WUSTL)
  摘要：This article addresses the problem of testing the conditional independence of two generic random vectors X and Y given a third random vector Z, which plays an important role in statistical and machine learning applications. We propose a new non-parametric testing procedure that avoids explicitly estimating any conditional distributions but instead requires sampling from the two marginal conditional distributions of X given Z and Y given Z. We further propose using a generative neural network (...
• Maozai Tian, Shuo Liu, and Tan Meng's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Tian, Maozai; Liu, Shuo; Meng, Tan
  作者单位：Renmin University of China; Xinjiang University of Finance & Economics; Changji University
• Principal stratification with U-statistics under principal ignorability AI翻译
  作者：Chen, Xinyuan; Li, Fan
  作者单位：Mississippi State University; Yale University; Yale University
  摘要：Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies in the relative ordering of potential outcomes within a principal stratum. We introduce the principal generalized causal effect estimands to accommodate nonlinear contrast functions, providing robust, probability-scale summaries suitable for ordinal outcome...