In this work, we consider the problem of goodness-of-fit (GoF) testing f...
Conformalized Quantile Regression (CQR) is a recently proposed method fo...
This paper studies distribution-free inference in settings where the dat...
Matrix completion aims to estimate missing entries in a data matrix, usi...
De Finetti's theorem, also called the de Finetti-Hewitt-Savage theorem, ...
Cross-validation (CV) is one of the most popular tools for assessing and...
In many modern statistical problems, the limited available data must be ...
Bagging is an important technique for stabilizing machine learning model...
This paper introduces a method that constructs valid and efficient lower...
Model-X knockoffs is a flexible wrapper method for high-dimensional
regr...
The field of distribution-free predictive inference provides tools for
p...
Permutation tests are an immensely popular statistical tool, used for te...
Conformal prediction is a popular, modern technique for providing valid
...
We consider linear structural equation models with latent variables and
...
Algorithmic stability is a concept from learning theory that expresses t...
In a binary classification problem where the goal is to fit an accurate
...
In data analysis problems where we are not able to rely on distributiona...
We consider a high-dimensional monotone single index model (hdSIM), whic...
Goodness-of-fit (GoF) testing is ubiquitous in statistics, with direct t...
For a regression problem with a binary label response, we examine the pr...
Ensemble learning is widely used in applications to make predictions in
...
The log-concave projection is an operator that maps a d-dimensional
dist...
We study the problem of estimation and testing in logistic regression wi...
An important factor to guarantee a fair use of data-driven recommendatio...
We study the bias of the isotonic regression estimator. While there is
e...
This paper introduces the jackknife+, which is a novel method for
constr...
We extend conformal prediction methodology beyond the case of exchangeab...
We consider the problem of distribution-free predictive inference, with ...
Consider a case-control study in which we have a random sample, construc...
We propose a general new method, the conditional permutation test, for
t...
Two methods are proposed for high-dimensional shape-constrained regressi...
Iterative thresholding algorithms seek to optimize a differentiable obje...
We consider the variable selection problem, which seeks to identify impo...
We analyze the performance of alternating minimization for loss function...
A significant literature studies ways of employing prior knowledge to im...
In many practical applications of multiple hypothesis testing using the ...