2期

最新文章

• From denoising diffusions to denoising Markov models 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)
  摘要：Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a Gaussian distribution and then learning to reverse this noising process to obtain synthetic datapoints. The denoising diffusion relies on approximations of the logarithmic derivatives of the noised data densities using score matching. Such models can also be used to perform approximate posterior simulation when one can only sample from the...
• Yingnian Wu and Weng Kee Wong's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Wu, Ying Nian; Wong, Weng Kee
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
• Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding AI翻译
  作者：Egami, Naoki; Tchetgen, Eric J. Tchetgen
  作者单位：Columbia University; University of Pennsylvania; University of Pennsylvania
  摘要：Identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmea...
• Tamás P. Papp, Paul Fearnhead, and Chris Sherlock's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Papp, Tamas P.; Fearnhead, Paul; Sherlock, Chris
  作者单位：Lancaster University; Lancaster 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
• 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
• Least squares estimation of a quasiconvex regression function AI翻译
  作者：Mukherjee, Somabha; Patra, Rohit K.; Johnson, Andrew L.; Morita, Hiroshi
  作者单位：National University of Singapore; State University System of Florida; University of Florida; Texas A&M University System; Texas A&M University College Station; University of Osaka
  摘要：We develop a new approach for the estimation of a multivariate function based on the economic axioms of quasiconvexity (and monotonicity). On the computational side, we prove the existence of the quasiconvex constrained least squares estimator (LSE) and provide a characterisation of the function space to compute the LSE via a mixed-integer quadratic programme. On the theoretical side, we provide finite sample risk bounds for the LSE via a sharp oracle inequality. Our results allow for errors t...
• Andi Wang'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
• Non-agency interventions for causal mediation in the presence of intermediate confounding AI翻译
  作者：Diaz, Ivan
  作者单位：New York University
  摘要：Recent approaches to causal inference have focused on causal effects defined as contrasts between the distribution of counterfactual outcomes under hypothetical interventions on the nodes of a graphical model. In this article, we develop theory for causal effects defined with respect to a different type of intervention, one which alters the information propagated through the edges of the graph. These information transfer interventions may be more useful than node interventions in settings in w...
• Johannes Schmidt-Hieber's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Schmidt-Hieber, Johannes
  作者单位：University of Twente