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06/05/2023
Conformal Prediction with Missing Values
Conformal prediction is a theoretically grounded framework for construct...
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05/17/2023
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Uncertainty quantification for inverse problems in imaging has drawn muc...
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02/14/2023
Derandomized Novelty Detection with FDR Control via Conformal E-values
Conformal prediction and other randomized model-free inference technique...
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02/14/2023
Lightsolver challenges a leading deep learning solver for Max-2-SAT problems
Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision...
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10/01/2022
Model-Free Sequential Testing for Conditional Independence via Testing by Betting
This paper develops a model-free sequential test for conditional indepen...
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09/08/2022
Quantum Sparse Coding
The ultimate goal of any sparse coding method is to accurately recover f...
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07/14/2022
From Shapley back to Pearson: Hypothesis Testing via the Shapley Value
Machine learning models, in particular artificial neural networks, are i...
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07/03/2022
Learning to Increase the Power of Conditional Randomization Tests
The model-X conditional randomization test is a generic framework for co...
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06/02/2022
Coordinated Double Machine Learning
Double machine learning is a statistical method for leveraging complex b...
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05/30/2022
Fast Nonlinear Vector Quantile Regression
Quantile regression (QR) is a powerful tool for estimating one or more c...
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05/12/2022
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Deep neural networks are powerful tools to detect hidden patterns in dat...
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02/10/2022
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Image-to-image regression is an important learning task, used frequently...
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10/28/2021
An ℓ^p-based Kernel Conditional Independence Test
We propose a new computationally efficient test for conditional independ...
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10/02/2021
Calibrated Multiple-Output Quantile Regression with Representation Learning
We develop a method to generate predictive regions that cover a multivar...
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06/08/2021
Searching for consistent associations with a multi-environment knockoff filter
This paper develops a method based on model-X knockoffs to find conditio...
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06/01/2021
Improving Conditional Coverage via Orthogonal Quantile Regression
We develop a method to generate prediction intervals that have a user-sp...
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05/18/2021
Conformal histogram regression
This paper develops a conformal method to compute prediction intervals f...
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04/16/2021
Testing for Outliers with Conformal p-values
This paper studies the construction of p-values for nonparametric outlie...
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06/08/2020
Achieving Equalized Odds by Resampling Sensitive Attributes
We present a flexible framework for learning predictive models that appr...
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06/03/2020
Classification with Valid and Adaptive Coverage
Conformal inference, cross-validation+, and the jackknife+ are hold-out ...
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08/15/2019
With Malice Towards None: Assessing Uncertainty via Equalized Coverage
An important factor to guarantee a fair use of data-driven recommendatio...
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05/08/2019
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction interval...
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11/16/2018
Deep Knockoffs
This paper introduces a machine for sampling approximate model-X knockof...
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06/26/2018
Improving Pursuit Algorithms Using Stochastic Resonance
Sparse Representation Theory is a sub-field of signal processing that ha...
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05/29/2018
Classification Stability for Sparse-Modeled Signals
Despite their impressive performance, deep convolutional neural networks...
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05/06/2018
Acceleration of RED via Vector Extrapolation
Models play an important role in inverse problems, serving as the prior ...
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08/29/2017
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) m...
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05/09/2017
Convolutional Dictionary Learning via Local Processing
Convolutional Sparse Coding (CSC) is an increasingly popular model in th...
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02/11/2017
On the Global-Local Dichotomy in Sparsity Modeling
The traditional sparse modeling approach, when applied to inverse proble...
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11/09/2016
The Little Engine that Could: Regularization by Denoising (RED)
Removal of noise from an image is an extensively studied problem in imag...
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09/23/2016
Example-Based Image Synthesis via Randomized Patch-Matching
Image and texture synthesis is a challenging task that has long been dra...
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07/27/2016
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
Convolutional neural networks (CNN) have led to many state-of-the-art re...
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06/03/2016
RAISR: Rapid and Accurate Image Super Resolution
Given an image, we wish to produce an image of larger size with signific...
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03/22/2016
Con-Patch: When a Patch Meets its Context
Measuring the similarity between patches in images is a fundamental buil...
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02/22/2015
