Zobrazeno 1 - 10
of 201
pro vyhledávání: '"Rush, Cynthia"'
Optimal transport and the Wasserstein distance $\mathcal{W}_p$ have recently seen a number of applications in the fields of statistics, machine learning, data science, and the physical sciences. These applications are however severely restricted by t
Externí odkaz:
http://arxiv.org/abs/2405.13153
Publikováno v:
2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, 2023, pp. 1-8
Power posteriors "robustify" standard Bayesian inference by raising the likelihood to a constant fractional power, effectively downweighting its influence in the calculation of the posterior. Power posteriors have been shown to be more robust to mode
Externí odkaz:
http://arxiv.org/abs/2310.07900
Autor:
Cademartori, Collin, Rush, Cynthia
Approximate Message Passing (AMP) algorithms are a class of iterative procedures for computationally-efficient estimation in high-dimensional inference and estimation tasks. Due to the presence of an 'Onsager' correction term in its iterates, for $N
Externí odkaz:
http://arxiv.org/abs/2302.00088
Random graph models with community structure have been studied extensively in the literature. For both the problems of detecting and recovering community structure, an interesting landscape of statistical and computational phase transitions has emerg
Externí odkaz:
http://arxiv.org/abs/2212.10872
We study the classical problem of predicting an outcome variable, $Y$, using a linear combination of a $d$-dimensional covariate vector, $\mathbf{X}$. We are interested in linear predictors whose coefficients solve: % \begin{align*} \inf_{\boldsymbol
Externí odkaz:
http://arxiv.org/abs/2211.07608
It is well known that central order statistics exhibit a central limit behavior and converge to a Gaussian distribution as the sample size grows. This paper strengthens this known result by establishing an entropic version of the CLT that ensures a s
Externí odkaz:
http://arxiv.org/abs/2205.04621
Publikováno v:
Annals of Statistics 2022
Sorted l1 regularization has been incorporated into many methods for solving high-dimensional statistical estimation problems, including the SLOPE estimator in linear regression. In this paper, we study how this relatively new regularization techniqu
Externí odkaz:
http://arxiv.org/abs/2105.13302
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become extremely popular in various structured high-dimensional statistical problems. The fact that the origins of these techniques can be traced back to notions of belief
Externí odkaz:
http://arxiv.org/abs/2105.02180
$\alpha$-posteriors and their variational approximations distort standard posterior inference by downweighting the likelihood and introducing variational approximation errors. We show that such distortions, if tuned appropriately, reduce the Kullback
Externí odkaz:
http://arxiv.org/abs/2104.08324
Publikováno v:
IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 1, pp. 21-36, March 2022
This paper considers the Gaussian multiple-access channel (MAC) in the asymptotic regime where the number of users grows linearly with the code length. We propose efficient coding schemes based on random linear models with approximate message passing
Externí odkaz:
http://arxiv.org/abs/2102.04730