8 citations · 20 across the 6 of their papers we have counts for
6 papers
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
Shai Feldman, Stephen Bates, Yaniv Romano
We introduce a framework for robust uncertainty quantification in situations where labeled training data are corrupted, through noisy or missing labels. We build on conformal predi…
Robust Conformal Prediction Using Privileged Information
Shai Feldman, Yaniv Romano
We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach…
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…
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
Or Feldman, Amit Boyarski, Shai Feldman +3
Graph isomorphism testing is usually approached via the comparison of graph invariants. Two popular alternatives that offer a good trade-off between expressive power and computatio…
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…
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…