作者：Xu, Yuanzhe; Mukherjee, Sumit
作者单位：Columbia University
摘要：In this paper, we derive the limit of experiments for one-parameter Ising models on dense regular graphs. In particular, we show that the limiting ex-periment is Gaussian in the low temperature regime, and non-Gaussian in the critical regime. We also derive the limiting distributions of the maxi-mum likelihood and maximum pseudolikelihood estimators, and study limit-ing power for tests of hypothesis against contiguous alternatives. To the best of our knowledge, this is the first attempt at est...
作者：Hazimeh, Hussein; Mazumder, Rahul; Radchenko, Peter
作者单位：Alphabet Inc.; Google Incorporated; Massachusetts Institute of Technology (MIT); University of Sydney
摘要：We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on convex relaxations and nonconvex heuristics, we focus on optimal solutions for the l(0)-regularized formulation, a problem that is relatively unexplored due to computational challenges. Our methodol-ogy covers both high-dimensional linear regression and nonparametric sparse additive modeling with smooth components. Ou...
