3 citations · 7 across the 11 of their papers we have counts for
11 papers
Testing hypotheses via orthogonalization
Ameer Dharamshi, Runjia Zou, Daniela Witten
Classical hypothesis testing frameworks break down in contemporary settings in which null hypotheses are increasingly abstract, the same data are used to both generate and test hyp…
Inference conditional on selection: a review
Anna Neufeld, Ronan Perry, Daniela Witten
In this article, we review selective inference, a set of techniques for inference when the statistical question asked is a function of the data. This setting often arises in contem…
Generalized Prediction-Powered Inference, with Application to Binary Classifier Evaluation
Runjia Zou, Daniela Witten, Brian Williamson
In the partially-observed outcome setting, a recent set of proposals known as "prediction-powered inference" (PPI) involve (i) applying a pre-trained machine learning model to pred…
Post-selection inference for penalized M-estimators via score thinning
Ronan Perry, Snigdha Panigrahi, Daniela Witten
We consider inference for M-estimators after model selection using a sparsity-inducing penalty. While existing methods for this task require bespoke inference procedures, we propos…
A Unified Framework for Semiparametrically Efficient Semi-Supervised Learning
Zichun Xu, Daniela Witten, Ali Shojaie
We consider statistical inference under a semi-supervised setting where we have access to both a labeled dataset consisting of pairs and an unlabeled datase…
Thinning a Wishart Random Matrix
Ameer Dharamshi, Anna Neufeld, Lucy L. Gao +2
Recent work has explored data thinning, a generalization of sample splitting that involves decomposing a (possibly matrix-valued) random variable into independent components. In th…
Decomposing Gaussians with Unknown Covariance
Ameer Dharamshi, Anna Neufeld, Lucy L. Gao +2
Common workflows in machine learning and statistics rely on the ability to partition the information in a data set into independent portions. Recent work has shown that this may be…
Discussion of "Data fission: splitting a single data point"
Anna Neufeld, Ameer Dharamshi, Lucy L. Gao +2
Leiner et al. [2023] introduce an important generalization of sample splitting, which they call data fission. They consider two cases of data fission: P1 fission and P2 fission. Wh…
Infer-and-widen, or not?
Ronan Perry, Zichun Xu, Olivia McGough +1
In recent years, there has been substantial interest in the task of selective inference: inference on a parameter that is selected from the data. Many of the existing proposals fal…
Inference on the proportion of variance explained in principal component analysis
Ronan Perry, Snigdha Panigrahi, Jacob Bien +1
Principal component analysis (PCA) is a longstanding and well-studied approach for dimension reduction. It rests upon the assumption that the underlying signal in the data has low…
The joint graphical lasso for inverse covariance estimation across multiple classes
Patrick Danaher, Pei Wang, Daniela M. Witten
We consider the problem of estimating multiple related but distinct graphical models on the basis of a high-dimensional data set with observations that belong to distinct classes.…