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Found 39 papers, 26 papers with code
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
With Malice Towards None: Assessing Uncertainty via Equalized Coverage
An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers.
The Little Engine that Could: Regularization by Denoising (RED)
As opposed to the $P^3$ method, we offer Regularization by Denoising (RED): using the denoising engine in defining the regularization of the inverse problem.
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Image-to-image regression is an important learning task, used frequently in biological imaging.
Deep Knockoffs
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.
Classification with Valid and Adaptive Coverage
Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm to construct prediction sets with guaranteed marginal coverage.
Testing for Outliers with Conformal p-values
We then introduce a new method to compute p-values that are both valid conditionally on the training data and independent of each other for different test points; this paves the way to stronger type-I error guarantees.
Conformal Prediction using Conditional Histograms
This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data.
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities.
Fast Nonlinear Vector Quantile Regression
Despite its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target $\boldsymbol{\mathrm{Y}}$ given the features $\boldsymbol{\mathrm{X}}$; (ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles; (iii) no fast or scalable solvers for VQR currently exist.
Calibrated Multiple-Output Quantile Regression with Representation Learning
We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability.
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Uncertainty quantification for inverse problems in imaging has drawn much attention lately.
Improving Conditional Coverage via Orthogonal Quantile Regression
To remedy this, we modify the loss function to promote independence between the size of the intervals and the indicator of a miscoverage event.
Conformal Prediction with Missing Values
This motivates our novel generalized conformalized quantile regression framework, missing data augmentation, which yields prediction intervals that are valid conditionally to the patterns of missing values, despite their exponential number.
Lightsolver challenges a leading deep learning solver for Max-2-SAT problems
Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision problem that is known to be NP-hard.
Achieving Risk Control in Online Learning Settings
To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk -- such as coverage of confidence intervals, false negative rate, or F1 score -- in the online setting.
Coordinated Double Machine Learning
Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with high-dimensional covariates, under the assumption of a partially linear model.
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing
As a result, we further our understanding of Shapley-based explanation methods from a novel perspective and characterize the conditions under which one can make statistically valid claims about feature importance via the Shapley value.
Semantic uncertainty intervals for disentangled latent spaces
Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on the street.
Model-X Sequential Testing for Conditional Independence via Testing by Betting
Our test can work with any sophisticated machine learning algorithm to enhance data efficiency to the extent possible.
Acceleration of RED via Vector Extrapolation
Models play an important role in inverse problems, serving as the prior for representing the original signal to be recovered.
Derandomized Novelty Detection with FDR Control via Conformal E-values
Conformal inference provides a general distribution-free method to rigorously calibrate the output of any machine learning algorithm for novelty detection.
Convolutional Dictionary Learning via Local Processing
Convolutional Sparse Coding (CSC) is an increasingly popular model in the signal and image processing communities, tackling some of the limitations of traditional patch-based sparse representations.
Early Time Classification with Accumulated Accuracy Gap Control
This data-driven rule attains finite-sample, distribution-free control of the accuracy gap between full and early-time classification.
Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals
Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations.
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
We show that the training of the filters is essential to allow for non-trivial signals in the model, and we derive an online algorithm to learn the dictionaries from real data, effectively resulting in cascaded sparse convolutional layers.
On the Global-Local Dichotomy in Sparsity Modeling
The traditional sparse modeling approach, when applied to inverse problems with large data such as images, essentially assumes a sparse model for small overlapping data patches.
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
This is shown to be tightly connected to CNN, so much so that the forward pass of the CNN is in fact the thresholding pursuit serving the ML-CSC model.
RAISR: Rapid and Accurate Image Super Resolution
Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts such as halos and noise amplification.
Example-Based Image Synthesis via Randomized Patch-Matching
Image and texture synthesis is a challenging task that has long been drawing attention in the fields of image processing, graphics, and machine learning.
Con-Patch: When a Patch Meets its Context
Therefore, with a minor increase of the dimensions (e. g. with additional 10 values to the patch representation), we implicitly/softly describe the information of a large patch.
Boosting of Image Denoising Algorithms
In this paper we propose a generic recursive algorithm for improving image denoising methods.
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance
The proposed method adds controlled noise to the input and estimates a sparse representation from the perturbed signal.
Adversarially Robust Conformal Prediction
By combining conformal prediction with randomized smoothing, our proposed method forms a prediction set with finite-sample coverage guarantee that holds for any data distribution with $\ell_2$-norm bounded adversarial noise, generated by any adversarial attack algorithm.
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
We propose a new conditional dependence measure and a statistical test for conditional independence.
Learning to Increase the Power of Conditional Randomization Tests
This is done by introducing a new cost function that aims at maximizing the test statistic used to measure violations of conditional independence.
Quantum Sparse Coding
The ultimate goal of any sparse coding method is to accurately recover from a few noisy linear measurements, an unknown sparse vector.
Conformal Prediction is Robust to Dispersive Label Noise
In such cases, we can also correct for noise of bounded size in the conformal prediction algorithm in order to ensure achieving the correct risk of the ground truth labels without score or data regularity.
