43 citations · 113 across the 10 of their papers we have counts for
12 papers
Revisiting inference after prediction
Keshav Motwani, Daniela Witten
Recent work has focused on the very common practice of prediction-based inference: that is, (i) using a pre-trained machine learning model to predict an unobserved response variabl…
Generalized Data Thinning Using Sufficient Statistics
Ameer Dharamshi, Anna Neufeld, Keshav Motwani +3
Our goal is to develop a general strategy to decompose a random variable into multiple independent random variables, without sacrificing any information about unknown parameter…
Data thinning for convolution-closed distributions
Anna Neufeld, Ameer Dharamshi, Lucy L. Gao +1
We propose data thinning, an approach for splitting an observation into two or more independent parts that sum to the original observation, and that follow the same distribution as…
Inferring independent sets of Gaussian variables after thresholding correlations
Arkajyoti Saha, Daniela Witten, Jacob Bien
We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. We assume that this set is selected via a very simple ap…
Inference after latent variable estimation for single-cell RNA sequencing data
Anna Neufeld, Lucy L. Gao, Joshua Popp +2
In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, rep…
Selective inference for k-means clustering
Yiqun T. Chen, Daniela M. Witten
We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering. In this setting, classical hypothesis tests lead to…
More powerful selective inference for the graph fused lasso
Yiqun T. Chen, Sean W. Jewell, Daniela M. Witten
The graph fused lasso -- which includes as a special case the one-dimensional fused lasso -- is widely used to reconstruct signals that are piecewise constant on a graph, meaning t…
Tree-Values: selective inference for regression trees
Anna C. Neufeld, Lucy L. Gao, Daniela M. Witten
We consider conducting inference on the output of the Classification and Regression Tree (CART) [Breiman et al., 1984] algorithm. A naive approach to inference that does not accoun…
SCALPEL: Extracting Neurons from Calcium Imaging Data
Ashley Petersen, Noah Simon, Daniela Witten
In the past few years, new technologies in the field of neuroscience have made it possible to simultaneously image activity in large populations of neurons at cellular resolution i…
Exact Spike Train Inference Via Optimization
Sean Jewell, Daniela Witten
In recent years, new technologies in neuroscience have made it possible to measure the activities of large numbers of neurons simultaneously in behaving animals. For each neuron, a…
Convex Modeling of Interactions with Strong Heredity
Asad Haris, Daniela Witten, Noah Simon
We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity. We propose FAMIL…
Fused Lasso Additive Model
Ashley Petersen, Daniela Witten, Noah Simon
We consider the problem of predicting an outcome variable using covariates that are measured on independent observations, in the setting in which flexible and interpretable…