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最新文章

• High-dimensional quantile regression: Convolution smoothing and concave regularization AI翻译
  作者：Tan, Kean Ming; Wang, Lan; Zhou, Wen-Xin
  作者单位：University of Michigan System; University of Michigan; University of Miami; University of California System; University of California San Diego
  摘要：l(1)-penalized quantile regression (QR) is widely used for analysing high-dimensional data with heterogeneity. It is now recognized that the l(1)-penalty introduces non-negligible estimation bias, while a proper use of concave regularization may lead to estimators with refined convergence rates and oracle properties as the signal strengthens. Although folded concave penalized M-estimation with strongly convex loss functions have been well studied, the extant literature on QR is relatively sile...
• A graph-theoretic approach to randomization tests of causal effects under general interference AI翻译
  作者：Puelz, David; Basse, Guillaume; Feller, Avi; Toulis, Panos
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Stanford University; University of California System; University of California Berkeley; University of Chicago
  摘要：Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighbouring streets. Classical randomization tests typically break down in this setting because many null hypotheses of interest are no longer sharp under interference. A promising alternative is to instead construct a conditional randomization test on a subset of units and assignments for which a given null hypothesis is shar...
• J. Goseling and MNM van Lieshout's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Goseling, J.; van Lieshout, M. N. M.
  作者单位：University of Twente; Centrum Wiskunde & Informatica (CWI)
• Sebastian Dietz's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Dietz, Sebastian
• Gaussian differential privacy AI翻译
  作者：Dong, Jinshuo; Roth, Aaron; Su, Weijie J.
  作者单位：University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：In the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analysing important primitives like privacy amplification by suhsampling. Inspired by the hypothesis testing formulation of privacy, this paper proposes a new relaxation of differential privacy, which w...
• Jorge Mateu's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Mateu, Jorge
  作者单位：Universitat Jaume I
• Gaussian differential privacy AI翻译
  作者：Balle, Borja
  作者单位：Alphabet Inc.; DeepMind