124 citations · 657 across the 40 of their papers we have counts for
8 papers · 1 filter
Robust Conformal Outlier Detection under Contaminated Reference Data
Meshi Bashari, Matteo Sesia, Yaniv Romano
Conformal prediction is a flexible framework for calibrating machine learning predictions, providing distribution-free statistical guarantees. In outlier detection, this calibratio…
Conformal Prediction with Missing Values
Margaux Zaffran, Aymeric Dieuleveut, Julie Josse +1
Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting t…
Coordinated Double Machine Learning
Nitai Fingerhut, Matteo Sesia, Yaniv Romano
Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with…
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia +1
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 re…
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
Meyer Scetbon, Laurent Meunier, Yaniv Romano
We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of tw…
Achieving Equalized Odds by Resampling Sensitive Attributes
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. This is achieved by introducing a general discrepan…
Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals
Yaniv Romano, Aviad Aberdam, Jeremias Sulam +1
Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations. These nuisances, which one can b…
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
Vardan Papyan, Yaniv Romano, Michael Elad
Convolutional neural networks (CNN) have led to many state-of-the-art results spanning through various fields. However, a clear and profound theoretical understanding of the forwar…