Conformal prediction is a theoretically grounded framework for construct...
Uncertainty quantification for inverse problems in imaging has drawn muc...
Conformal prediction and other randomized model-free inference technique...
Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision...
This paper develops a model-free sequential test for conditional
indepen...
The ultimate goal of any sparse coding method is to accurately recover f...
Machine learning models, in particular artificial neural networks, are
i...
The model-X conditional randomization test is a generic framework for
co...
Double machine learning is a statistical method for leveraging complex
b...
Quantile regression (QR) is a powerful tool for estimating one or more
c...
Deep neural networks are powerful tools to detect hidden patterns in dat...
Image-to-image regression is an important learning task, used frequently...
We propose a new computationally efficient test for conditional independ...
We develop a method to generate predictive regions that cover a multivar...
This paper develops a method based on model-X knockoffs to find conditio...
We develop a method to generate prediction intervals that have a
user-sp...
This paper develops a conformal method to compute prediction intervals f...
This paper studies the construction of p-values for nonparametric outlie...
We present a flexible framework for learning predictive models that
appr...
Conformal inference, cross-validation+, and the jackknife+ are hold-out
...
An important factor to guarantee a fair use of data-driven recommendatio...
Conformal prediction is a technique for constructing prediction interval...
This paper introduces a machine for sampling approximate model-X knockof...
Sparse Representation Theory is a sub-field of signal processing that ha...
Despite their impressive performance, deep convolutional neural networks...
Models play an important role in inverse problems, serving as the prior ...
The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) m...
Convolutional Sparse Coding (CSC) is an increasingly popular model in th...
The traditional sparse modeling approach, when applied to inverse proble...
Removal of noise from an image is an extensively studied problem in imag...
Image and texture synthesis is a challenging task that has long been dra...
Convolutional neural networks (CNN) have led to many state-of-the-art re...
Given an image, we wish to produce an image of larger size with signific...
Measuring the similarity between patches in images is a fundamental buil...
In this paper we propose a generic recursive algorithm for improving ima...