1期

最新文章

• Priyantha Wijayatunga's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Wijayatunga, Priyantha
  作者单位：Umea University
• Usable and precise asymptotics for generalized linear mixed model analysis and design AI翻译
  作者：Jiang, Jiming; Wand, Matt P.; Bhaskaran, Aishwarya
  作者单位：University of California System; University of California Davis; University of Technology Sydney
  摘要：We derive precise asymptotic results that are directly usable for confidence intervals and Wald hypothesis tests for likelihood-based generalized linear mixed model analysis. The essence of our approach is to derive the exact leading term behaviour of the Fisher information matrix when both the number of groups and number of observations within each group diverge. This leads to asymptotic normality results with simple studentizable forms. Similar analyses result in tractable leading term forms...
• Inferential Wasserstein generative adversarial networks AI翻译
  作者：Chen, Yao; Gao, Qingyi; Wang, Xiao
  作者单位：Purdue University System; Purdue University
  摘要：Generative adversarial networks (GANs) have been impactful on many problems and applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the Wasserstein distance to avoid the caveats in the minmax two-player training of GANs but has other defects such as mode collapse and lack of metric to detect the convergence. We introduce a novel inferential Wasserstein GAN (iWGAN) model, which is a principled framework to fuse autoencoders and WGANs. The iWGAN model jointly lea...
• Waste-free sequential Monte Carlo AI翻译
  作者：Dau, Hai-Dang; Chopin, Nicolas
  作者单位：Institut Polytechnique de Paris; ENSAE Paris; Institut Polytechnique de Paris
  摘要：A standard way to move particles in a sequential Monte Carlo (SMC) sampler is to apply several steps of a Markov chain Monte Carlo (MCMC) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asympto...
• High-dimensional, multiscale online changepoint detection AI翻译
  作者：Chen, Yudong; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; University of London; London School Economics & Political Science; University of London; University College London
  摘要：We introduce a new method for high-dimensional, online changepoint detection in settings where a p-variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of different scales in each coordinate, and then aggregating test statistics across scales and coordinates. The algorithm is online in the sense that both its storage requirements and worstcase computational complexity per new observation are independent ...
• Gaussian Differential Privacy AI翻译
  作者：Chai, Christine P.
  作者单位：Microsoft
• Gaussian Differential Privacy AI翻译
  作者：Krusche, Peter; Bretz, Frank
  作者单位：Novartis
• Seconder of the vote of thanks to Dong et al. and contribution to the Discussion of 'Gaussian Differential Privacy' AI翻译
  作者：Avella-Medina, Marco
  作者单位：Columbia University
• Authors' reply to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Dong, Jinshuo; Roth, Aaron; Su, Weijie J.
  作者单位：University of Pennsylvania
• Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality AI翻译
  作者：Li, Sai; Cai, T. Tony; Li, Hongzhe
  作者单位：University of Pennsylvania; University of Pennsylvania
  摘要：This paper considers estimation and prediction of a high-dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models are available. When the set of informative auxiliary studies is known, an estimator and a predictor are proposed and their optimality is established. The optimal rates of convergence for prediction and estimation are faster than the correspondin...