6 papers
Prediction-Powered Active Testing
Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang +2
Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit th…
A Generative Model for Extremely Sparse Edge-Exchangeable Networks
Valentin Kilian
We propose a graph generative model for sequences of extremely sparse, edge-exchangeable networks. Models for sparse graphs often face a trade-off between desirable properties like…
Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means
Valentin Kilian, Stefano Cortinovis, François Caron
Confidence sequences based on test martingales provide time-uniform uncertainty quantification for the mean of bounded IID observations without parametric distributional assumption…
Confidence sequences with informative, bounded-influence priors
Stefano Cortinovis, Valentin Kilian, François Caron
Confidence sequences are collections of confidence regions that simultaneously cover the true parameter for every sample size at a prescribed confidence level. Tightening these seq…
Anytime-valid, Bayes-assisted, Prediction-Powered Inference
Valentin Kilian, Stefano Cortinovis, François Caron
Given a large pool of unlabelled data and a smaller amount of labels, prediction-powered inference (PPI) leverages machine learning predictions to increase the statistical efficien…
Rapidly Varying Completely Random Measures for Modeling Extremely Sparse Networks
Valentin Kilian, Benjamin Guedj, François Caron
Completely random measures (CRMs) are fundamental to Bayesian nonparametric models, with applications in clustering, feature allocation, and network analysis. A key quantity of int…