124 citations · 657 across the 40 of their papers we have counts for
42 papers
Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints
Shai Feldman, Yaniv Romano
We evaluate large language models (LLMs) in multi-turn interactions through their time-to-event: the number of interaction steps required to produce an event of interest, such as a…
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…
Pivotal Auto-Encoder via Self-Normalizing ReLU
Nelson Goldenstein, Jeremias Sulam, Yaniv Romano
Sparse auto-encoders are useful for extracting low-dimensional representations from high-dimensional data. However, their performance degrades sharply when the input noise at test…
Early Time Classification with Accumulated Accuracy Gap Control
Liran Ringel, Regev Cohen, Daniel Freedman +2
Early time classification algorithms aim to label a stream of features without processing the full input stream, while maintaining accuracy comparable to that achieved by applying…
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…
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Omer Belhasin, Yaniv Romano, Daniel Freedman +2
Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable value…
Lightsolver challenges a leading deep learning solver for Max-2-SAT problems
Hod Wirzberger, Assaf Kalinski, Idan Meirzada +4
Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision problem that is known to be NP-hard. In this paper, we compare LightSolver's quantum-inspired algorithm to…
Derandomized Novelty Detection with FDR Control via Conformal E-values
Meshi Bashari, Amir Epstein, Yaniv Romano +1
Conformal inference provides a general distribution-free method to rigorously calibrate the output of any machine learning algorithm for novelty detection. While this approach has…
Model-X Sequential Testing for Conditional Independence via Testing by Betting
Shalev Shaer, Gal Maman, Yaniv Romano
This paper develops a model-free sequential test for conditional independence. The proposed test allows researchers to analyze an incoming i.i.d. data stream with any arbitrary dep…
Label Noise Robustness of Conformal Prediction
Bat-Sheva Einbinder, Shai Feldman, Stephen Bates +3
We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, c…
Semantic uncertainty intervals for disentangled latent spaces
Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates +2
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 t…
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing
Jacopo Teneggi, Beepul Bharti, Yaniv Romano +1
The complex nature of artificial neural networks raises concerns on their reliability, trustworthiness, and fairness in real-world scenarios. The Shapley value -- a solution concep…
Learning to Increase the Power of Conditional Randomization Tests
Shalev Shaer, Yaniv Romano
The model-X conditional randomization test is a generic framework for conditional independence testing, unlocking new possibilities to discover features that are conditionally asso…
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…
Fast Nonlinear Vector Quantile Regression
Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano +1
Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable given explanatory features .…
Achieving Risk Control in Online Learning Settings
Shai Feldman, Liran Ringel, Stephen Bates +1
To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk -- such as coverage o…
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…
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Anastasios N Angelopoulos, Amit P Kohli, Stephen Bates +5
Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that prot…
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…
Calibrated Multiple-Output Quantile Regression with Representation Learning
Shai Feldman, Stephen Bates, Yaniv Romano
We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we…
Searching for consistent associations with a multi-environment knockoff filter
Shuangning Li, Matteo Sesia, Yaniv Romano +2
This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate. The…
Improving Conditional Coverage via Orthogonal Quantile Regression
Shai Feldman, Stephen Bates, Yaniv Romano
We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space, a property called conditional coverage. A typica…
Conformal Prediction using Conditional Histograms
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. Leveraging black-box machine learn…
Testing for Outliers with Conformal p-values
Stephen Bates, Emmanuel Candès, Lihua Lei +2
This paper studies the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The goal is to test whether new independent samples belo…