Search Results for author:
Found 5 papers, 4 papers with code
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.
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.
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
Two popular alternatives that offer a good trade-off between expressive power and computational efficiency are combinatorial (i. e., obtained via the Weisfeiler-Leman (WL) test) and spectral invariants.
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.
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.
