Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Soloff Jake A."'
We introduce a method for performing cross-validation without sample splitting. The method is well-suited for problems where traditional sample splitting is infeasible, such as when data are not assumed to be independently and identically distributed
Externí odkaz:
http://arxiv.org/abs/2412.14423
Model selection is the process of choosing from a class of candidate models given data. For instance, methods such as the LASSO and sparse identification of nonlinear dynamics (SINDy) formulate model selection as finding a sparse solution to a linear
Externí odkaz:
http://arxiv.org/abs/2410.18268
We propose a new framework for algorithmic stability in the context of multiclass classification. In practice, classification algorithms often operate by first assigning a continuous score (for instance, an estimated probability) to each possible lab
Externí odkaz:
http://arxiv.org/abs/2405.14064
Model averaging techniques based on resampling methods (such as bootstrapping or subsampling) have been utilized across many areas of statistics, often with the explicit goal of promoting stability in the resulting output. We provide a general, finit
Externí odkaz:
http://arxiv.org/abs/2405.09511
Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing differen
Externí odkaz:
http://arxiv.org/abs/2307.03748
Bagging is an important technique for stabilizing machine learning models. In this paper, we derive a finite-sample guarantee on the stability of bagging for any model. Our result places no assumptions on the distribution of the data, on the properti
Externí odkaz:
http://arxiv.org/abs/2301.12600
Despite the popularity of the false discovery rate (FDR) as an error control metric for large-scale multiple testing, its close Bayesian counterpart the local false discovery rate (lfdr), defined as the posterior probability that a particular null hy
Externí odkaz:
http://arxiv.org/abs/2207.07299
Consider the relationship between a regulator (the principal) and an experimenter (the agent) such as a pharmaceutical company. The pharmaceutical company wishes to sell a drug for profit, whereas the regulator wishes to allow only efficacious drugs
Externí odkaz:
http://arxiv.org/abs/2205.06812
Multivariate, heteroscedastic errors complicate statistical inference in many large-scale denoising problems. Empirical Bayes is attractive in such settings, but standard parametric approaches rest on assumptions about the form of the prior distribut
Externí odkaz:
http://arxiv.org/abs/2109.03466
We study the problem of high-dimensional covariance estimation under the constraint that the partial correlations are nonnegative. The sign constraints dramatically simplify estimation: the Gaussian maximum likelihood estimator is well defined with o
Externí odkaz:
http://arxiv.org/abs/2007.15252