Search Results for author: Yaniv Romano

Found 26 papers, 15 papers with code

Conformalized Online Learning: Online Calibration Without a Holdout Set

1 code implementation18 May 2022 Shai Feldman, Stephen Bates, Yaniv Romano

We develop a framework for constructing uncertainty sets with a valid coverage guarantee in an online setting, in which the underlying data distribution can drastically -- and even adversarially -- shift over time.

online learning

Training Uncertainty-Aware Classifiers with Conformalized Deep Learning

1 code implementation12 May 2022 Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou

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.

An $\ell^p$-based Kernel Conditional Independence Test

no code implementations28 Oct 2021 Meyer Scetbon, Laurent Meunier, Yaniv Romano

We propose a new computationally efficient test for conditional independence based on the $L^{p}$ distance between two kernel-based representatives of well suited distributions.

Calibrated Multiple-Output Quantile Regression with Representation Learning

1 code implementation2 Oct 2021 Shai Feldman, Stephen Bates, Yaniv Romano

Second, we propose an extension of conformal prediction to the multivariate response setting that modifies any method to return sets with a pre-specified coverage level.

Adversarially Robust Conformal Prediction

no code implementations ICLR 2022 Asaf Gendler, Tsui-Wei Weng, Luca Daniel, Yaniv Romano

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.

Adversarial Attack

Improving Conditional Coverage via Orthogonal Quantile Regression

1 code implementation NeurIPS 2021 Shai Feldman, Stephen Bates, Yaniv Romano

To remedy this, we modify the loss function to promote independence between the size of the intervals and the indicator of a miscoverage event.

Prediction Intervals

Conformal Prediction using Conditional Histograms

1 code implementation NeurIPS 2021 Matteo Sesia, Yaniv Romano

This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data.

Prediction Intervals

Testing for Outliers with Conformal p-values

1 code implementation16 Apr 2021 Stephen Bates, Emmanuel Candès, Lihua Lei, Yaniv Romano, Matteo Sesia

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.

Outlier Detection

Achieving Equalized Odds by Resampling Sensitive Attributes

1 code implementation NeurIPS 2020 Yaniv Romano, Stephen Bates, Emmanuel J. Candès

We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness.

Fairness Multi-class Classification +1

Classification with Valid and Adaptive Coverage

1 code implementation NeurIPS 2020 Yaniv Romano, Matteo Sesia, Emmanuel J. Candès

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.

Classification General Classification

With Malice Towards None: Assessing Uncertainty via Equalized Coverage

1 code implementation15 Aug 2019 Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, Emmanuel J. Candès

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.

Prediction Intervals Recommendation Systems

Conformalized Quantile Regression

4 code implementations NeurIPS 2019 Yaniv Romano, Evan Patterson, Emmanuel J. Candès

Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.

Prediction Intervals

Deep Knockoffs

4 code implementations16 Nov 2018 Yaniv Romano, Matteo Sesia, Emmanuel J. Candès

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.

Variable Selection

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance

no code implementations26 Jun 2018 Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad

The proposed method adds controlled noise to the input and estimates a sparse representation from the perturbed signal.

Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals

no code implementations29 May 2018 Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad

Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations.

General Classification

Acceleration of RED via Vector Extrapolation

1 code implementation6 May 2018 Tao Hong, Yaniv Romano, Michael Elad

Models play an important role in inverse problems, serving as the prior for representing the original signal to be recovered.


Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning

no code implementations29 Aug 2017 Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad

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.

Dictionary Learning

Convolutional Dictionary Learning via Local Processing

1 code implementation ICCV 2017 Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad

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.

Dictionary Learning Image Inpainting

On the Global-Local Dichotomy in Sparsity Modeling

no code implementations11 Feb 2017 Dmitry Batenkov, Yaniv Romano, Michael Elad

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.

The Little Engine that Could: Regularization by Denoising (RED)

2 code implementations9 Nov 2016 Yaniv Romano, Michael Elad, Peyman Milanfar

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.

Deblurring Image Deblurring +2

Example-Based Image Synthesis via Randomized Patch-Matching

no code implementations23 Sep 2016 Yi Ren, Yaniv Romano, Michael Elad

Image and texture synthesis is a challenging task that has long been drawing attention in the fields of image processing, graphics, and machine learning.

Image Generation Patch Matching +1

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding

no code implementations27 Jul 2016 Vardan Papyan, Yaniv Romano, Michael Elad

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

no code implementations3 Jun 2016 Yaniv Romano, John Isidoro, Peyman Milanfar

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.

Image Super-Resolution

Con-Patch: When a Patch Meets its Context

no code implementations22 Mar 2016 Yaniv Romano, Michael Elad

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.

Image Denoising Image Super-Resolution

Boosting of Image Denoising Algorithms

no code implementations22 Feb 2015 Yaniv Romano, Michael Elad

In this paper we propose a generic recursive algorithm for improving image denoising methods.

Image Denoising

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