2024

最新文章

• Maozai Tian, Keming Yu and Jiangfeng Wang's contribution to the Discussion of 'Safe testing' by Grünwald, De Heide, and Koolen AI翻译
  作者：Tian, Maozai; Yu, Keming; Wang, Jiangfeng
  作者单位：Renmin University of China; Zhejiang Gongshang University; Brunel University
• Bo Zhang's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Zhang, Bo
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS
• Pietro Rigo's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes and Walker AI翻译
  作者：Fong, Edwin; Holmes, Chris; Walker, Stephen G.
  作者单位：Alan Turing Institute; University of Oxford; University of Texas System; University of Texas Austin; University of Oxford
• Authors' reply to the Discussion of 'Martingale Posterior Distributions' AI翻译
  作者：Fong, Edwin; Holmes, Chris; Walker, Stephen G.
  作者单位：Alan Turing Institute; University of Oxford; University of Texas System; University of Texas Austin
• Kolyan Ray and Botond Szabo's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes and Walker AI翻译
  作者：Ray, Kolyan; Szabo, Botond
  作者单位：Imperial College London; Bocconi University
• Maxway CRT: improving the robustness of the model-X inference AI翻译
  作者：Li, Shuangning; Liu, Molei
  作者单位：Harvard University
  摘要：The model-X conditional randomisation test (CRT) is a flexible and powerful testing procedure for testing the hypothesis X?Y divide Z. However, it requires perfect knowledge of X divide Z and may lose its validity when there is an error in modelling X divide Z. This problem is even more severe when Z is of high dimensionality. In response to this, we propose the Maxway CRT, which learns the distribution of Y divide Z and uses it to calibrate the resampling distribution of X to gain robustness ...
• Prediction sets adaptive to unknown covariate shift AI翻译
  作者：Qiu, Hongxiang; Dobriban, Edgar; Tchetgen, Eric Tchetgen
  作者单位：University of Pennsylvania
  摘要：Predicting sets of outcomes-instead of unique outcomes-is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructing prediction sets with statistical guarantees, adapting to unknown covariate shift-a prevalent issue in practice-poses a serious unsolved challenge. In this article, we show that prediction sets with finite-sample coverage guarantee are uninformative and propose a novel flexible distribution-free method, PredSet-1Step, to ...
• Holdout predictive checks for Bayesian model criticism (vol 86, pg 194, 2024) AI翻译
  作者：Moran, Gemma E.; Blei, David M.; Ranganath, Rajesh
• Samuel Pawel and Leonhard Held's contribution to the Discussion of 'Safe Testing' by Grunwald, de Heide, and Koolen AI翻译
  作者：Pawel, Samuel; Held, Leonhard
  作者单位：University of Zurich; Swiss School of Public Health (SSPH+)
• MNM van Lieshout and C. Lu's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：van Lieshout, Marie-Colette; Lu, Changqing
  作者单位：Centrum Wiskunde & Informatica (CWI); University of Twente