2期

最新文章

• Authors' reply to the Discussion of 'From denoising diffusions to denoising Markov models' at the Discussion Meeting on 'Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Benton, Joe; Shi, Yuyang; De Bortoli, Valentin; Deligiannidis, George; Doucet, Arnaud
  作者单位：University of Oxford; Universite PSL; Ecole Normale Superieure (ENS)
• Ayla Jungbluth and Johannes Lederer's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Jungbluth, Ayla; Lederer, Johannes
  作者单位：Ruhr University Bochum
• Hernando Ombao's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Li, Jie; Fearnhead, Paul; Fryzlewicz, Piotr; Wang, Tengyao
  作者单位：University of London; London School Economics & Political Science; Lancaster University
  摘要：Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated b...
• Pierre-Aurelien Gilliot, Christophe Andrieu, Anthony Lee, Song Liu, and Michael Whitehouse's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Gilliot, Pierre-Aurelien; Andrieu, Christophe; Lee, Anthony; Liu, Song; Whitehouse, Michael
  作者单位：University of Bristol
• Yongmiao Hong, Oliver Linton, Jiajing Sun, and Meiting Zhu's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Hong, Yongmiao; Linton, Oliver; Sun, Jiajing; Zhu, Meiting
  作者单位：Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Cambridge; University of Birmingham; Xiamen University
• Authors' reply to the Discussion of 'Automatic change-point detection in time series via deep learning' at the Discussion Meeting on 'Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Li, Jie; Fearnhead, Paul; Fryzlewicz, Piotr; Wang, Tengyao
  作者单位：University of London; London School Economics & Political Science; Lancaster University
• Ensemble methods for testing a global null AI翻译
  作者：Liu, Yaowu; Liu, Zhonghua; Lin, Xihong
  作者单位：Southwestern University of Finance & Economics - China; Columbia University; Harvard University; Harvard University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. In this paper, motivated by the success of ensemble learning methods for prediction or c...
• Yudong Chen and Yining Chen's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Chen, Yudong; Chen, Yining
  作者单位：University of London; London School Economics & Political Science
• James Jackson's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Jackson, James
  作者单位：Alan Turing Institute
  摘要：Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our approach is motivated b...
• Ivor Cribben and Anastasiou Andreas's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Anastasiou, Andreas; Cribben, Ivor
  作者单位：University of Cyprus; University of Alberta